Switching between fault response models in a storage system

ABSTRACT

A storage system switching between mediation models within a storage system, where the switching between mediation models includes: determining, among one or more of the plurality of storage systems, a change in availability of a mediator service, wherein one or more of the plurality of storage systems are configured to request mediation from the mediator service in response to a fault; and communicating, among the plurality of storage systems and responsive to determining the change in availability of the mediator service, a fault response model to be used as an alternate to the mediator service among one or more of the plurality of storage systems.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation application for patent entitled to a filing dateand claiming the benefit of earlier-filed U.S. Pat. No. 11,677,687,issued Jun. 13, 2023, which is a continuation of U.S. Pat. No.10,992,598, issued Apr. 27, 2021, which is a non-provisional applicationfor patent entitled to a filing date and claiming the benefit ofearlier-filed: U.S. Provisional Patent Application Ser. No. 62/674,570,filed May 21, 2018, and U.S. Provisional Patent Application Ser. No.62/695,433, filed Jul. 9, 2018; each of which is herein incorporated byreference in its entirety.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 4 sets forth a block diagram illustrating a plurality of storagesystems that support a pod according to some embodiments of the presentdisclosure.

FIG. 5 sets forth a block diagram illustrating a plurality of storagesystems that support a pod according to some embodiments of the presentdisclosure.

FIG. 6 sets forth a block diagram illustrating a plurality of storagesystems that support a pod according to some embodiments of the presentdisclosure.

FIG. 7 sets forth diagrams of metadata representations that may beimplemented as a structured collection of metadata objects that mayrepresent a logical volume of storage data, or a portion of a logicalvolume, in accordance with some embodiments of the present disclosure.

FIG. 8 sets forth a flow chart illustrating an example method ofmediation between storage systems according to some embodiments of thepresent disclosure.

FIG. 9 sets forth a flow chart illustrating an example method ofswitching between fault response models within a storage systemsynchronously replicating data according to some embodiments of thepresent disclosure.

FIG. 10 sets forth a flow chart illustrating an example method ofswitching between fault response models within a storage systemsynchronously replicating data according to some embodiments of thepresent disclosure.

FIG. 11 sets forth a flow chart illustrating an example method ofswitching between fault response models within a storage systemsynchronously replicating data according to some embodiments of thepresent disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatus, and products for switching between faultresponse models within a storage system synchronously replicating datain accordance with embodiments of the present disclosure are describedwith reference to the accompanying drawings, beginning with FIG. 1A.FIG. 1A illustrates an example system for data storage, in accordancewith some implementations. System 100 (also referred to as “storagesystem” herein) includes numerous elements for purposes of illustrationrather than limitation. It may be noted that system 100 may include thesame, more, or fewer elements configured in the same or different mannerin other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (′IF), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110 (also referred to as“controller” herein). A storage array controller 110 may be embodied asa module of automated computing machinery comprising computer hardware,computer software, or a combination of computer hardware and software.In some implementations, the storage array controllers 110 may beconfigured to carry out various storage tasks. Storage tasks may includewriting data received from the computing devices 164A-B to storage array102A-B, erasing data from storage array 102A-B, retrieving data fromstorage array 102A-B and providing data to computing devices 164A-B,monitoring and reporting of disk utilization and performance, performingredundancy operations, such as Redundant Array of Independent Drives(RAID′) or RAID-like data redundancy operations, compressing data,encrypting data, and so forth.

Storage array controller 110 may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110 mayinclude, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110 may be independentlycoupled to the LAN 160. In implementations, storage array controller 110may include an I/O controller or the like that couples the storage arraycontroller 110 for data communications, through a midplane (not shown),to a persistent storage resource 170A-B (also referred to as a “storageresource” herein). The persistent storage resource 170A-B main includeany number of storage drives 171A-F (also referred to as “storagedevices” herein) and any number of non-volatile Random Access Memory(‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110, data to be stored in the storage drives 171A-F. In someexamples, the data may originate from computing devices 164A-B. In someexamples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110 may be configured toutilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110 writes data directly tothe storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110 may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110 may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110, thenumber of program-erase (‘P/E’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110 in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110 may issuea command to locate memory blocks that contain control information. Itmay be noted that control information may be so large that parts of thecontrol information may be stored in multiple locations, that thecontrol information may be stored in multiple locations for purposes ofredundancy, for example, or that the control information may otherwisebe distributed across multiple memory blocks in the storage drive171A-F.

In implementations, storage array controllers 110 may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110 querying the storage drives 171A-F for the locationof control information for a particular storage drive 171A-F. Thestorage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110 thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110 may issue a request to read data stored at the addressassociated with the location of control information for the storagedrives 171A-F.

In other implementations, the storage array controllers 110 may furtheroffload device management responsibilities from storage drives 171A-F byperforming, in response to receiving the control information, a storagedrive management operation. A storage drive management operation mayinclude, for example, an operation that is typically performed by thestorage drive 171A-F (e.g., the controller (not shown) associated with aparticular storage drive 171A-F). A storage drive management operationmay include, for example, ensuring that data is not written to failedmemory blocks within the storage drive 171A-F, ensuring that data iswritten to memory blocks within the storage drive 171A-F in such a waythat adequate wear leveling is achieved, and so forth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110 (e.g.,storage array controller 110A) of a storage system 100 may be designatedwith primary status (also referred to as “primary controller” herein),and other storage array controllers 110 (e.g., storage array controller110A) may be designated with secondary status (also referred to as“secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110 may change. For example, storagearray controller 110A may be designated with secondary status, andstorage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110 are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110 may becoupled to the midplane via one or more data communication links and themidplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may similar to the storage array controllers 110 described withrespect to FIG. 1A. In one example, storage array controller 101 may besimilar to storage array controller 110A or storage array controller110B. Storage array controller 101 includes numerous elements forpurposes of illustration rather than limitation. It may be noted thatstorage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (RISC′) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an application specificintegrated circuit (‘ASIC’), a field programmable gate array (‘FPGA’), adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in a direct-mapped flash storagesystem. In one embodiment, a direct-mapped flash storage system is onethat addresses data blocks within flash drives directly and without anaddress translation performed by the storage controllers of the flashdrives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storagecontroller 119. In one embodiment, storage controller 119 may be a CPU,ASIC, FPGA, or any other circuitry that may implement control structuresnecessary according to the present disclosure. In one embodiment, system117 includes flash memory devices (e.g., including flash memory devices120 a-n), operatively coupled to various channels of the storage devicecontroller 119. Flash memory devices 120 a-n, may be presented to thecontroller 119 as an addressable collection of Flash pages, eraseblocks, and/or control elements sufficient to allow the storage devicecontroller 119 to program and retrieve various aspects of the Flash. Inone embodiment, storage device controller 119 may perform operations onflash memory devices 120A-N including storing and retrieving datacontent of pages, arranging and erasing any blocks, tracking statisticsrelated to the use and reuse of Flash memory pages, erase blocks, andcells, tracking and predicting error codes and faults within the Flashmemory, controlling voltage levels associated with programming andretrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119 or multiple storage devicecontrollers. The RAM 121 may be utilized for other purposes as well,such as temporary program memory for a processing device (e.g., a CPU)in the storage device controller 119.

In one embodiment, system 119 may include a stored energy device 122,such as a rechargeable battery or a capacitor. The Stored energy device122 may store energy sufficient to power the storage device controller119, some amount of the RAM (e.g., RAM 121), and some amount of Flashmemory (e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119 may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119 from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119 may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119 may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices107 a-120 n stored energy device 122 may power storage device controller119 and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the storage energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example system 124 for data storage inaccordance with some implementations. In one embodiment, system 124includes storage controllers 125 a, 125 b. In one embodiment, storagecontrollers 125 a, 125 b are operatively coupled to Dual PCI storagedevices 119 a, 119 b and 119 c, 119 d, respectively. Storage controllers125 a, 125 b may be operatively coupled (e.g., via a storage network130) to some number of host computers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices 119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, controllers 125 a, 125 b operate as PCI masters toone or the other PCI buses 128 a, 128 b. In another embodiment, 128 aand 128 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Other storage system embodiments mayoperate storage controllers 125 a, 125 b as multi-masters for both PCIbuses 128 a, 128 b. Alternately, a PCI/NVMe/NVMf switchinginfrastructure or fabric may connect multiple storage controllers. Somestorage system embodiments may allow storage devices to communicate witheach other directly rather than communicating only with storagecontrollers. In one embodiment, a storage device controller 119 a may beoperable under direction from a storage controller 125 a to synthesizeand transfer data to be stored into Flash memory devices from data thathas been stored in RAM (e.g., RAM 121 of FIG. 1C). For example, arecalculated version of RAM content may be transferred after a storagecontroller has determined that an operation has fully committed acrossthe storage system, or when fast-write memory on the device has reacheda certain used capacity, or after a certain amount of time, to ensureimprove safety of the data or to release addressable fast-write capacityfor reuse. This mechanism may be used, for example, to avoid a secondtransfer over a bus (e.g., 128 a, 128 b) from the storage controllers125 a, 125 b. In one embodiment, a recalculation may include compressingdata, attaching indexing or other metadata, combining multiple datasegments together, performing erasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one controller 125 a to another controller 125 b, or it couldbe used to offload compression, data aggregation, and/or erasure codingcalculations and transfers to storage devices to reduce load on storagecontrollers or the storage controller interface 129 a, 129 b to the PCIbus 128 a, 128 b.

A storage device controller 119 may include mechanisms for implementinghigh availability primitives for use by other parts of a storage systemexternal to the Dual PCI storage device 118. For example, reservation orexclusion primitives may be provided so that, in a storage system withtwo storage controllers providing a highly available storage service,one storage controller may prevent the other storage controller fromaccessing or continuing to access the storage device. This could beused, for example, in cases where one controller detects that the othercontroller is not functioning properly or where the interconnect betweenthe two storage controllers may itself not be functioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one or more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration between storagedevices in accordance with pages that are no longer needed as well as tomanage Flash page and erase block lifespans and to manage overall systemperformance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storageunits 152 or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 171A-Fand power distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 171A-F can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 171A-F can be included in or implementedwith a top of rack switch, in some embodiments. As illustrated in FIG.2B, storage cluster 161 is enclosed within a single chassis 138.External port 176 is coupled to storage nodes 150 through communicationsinterconnect 171A-F, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storages 152, forexample as lists or other data structures stored in memory. In someembodiments the authorities are stored within the non-volatile solidstate storage 152 and supported by software executing on a controller orother processor of the non-volatile solid state storage 152. In afurther embodiment, authorities 168 are implemented on the storage nodes150, for example as lists or other data structures stored in the memory154 and supported by software executing on the CPU 156 of the storagenode 150. Authorities 168 control how and where data is stored in thenon-volatile solid state storages 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of Mode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storages 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an Mode number or a data segment number. Theoutput of this operation points to a non-volatile solid state storage152 having the authority 168 for that particular piece of data. In someembodiments there are two stages to this operation. The first stage mapsan entity identifier (ID), e.g., a segment number, Mode number, ordirectory number to an authority identifier. This mapping may include acalculation such as a hash or a bit mask. The second stage is mappingthe authority identifier to a particular non-volatile solid statestorage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or Mode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Modes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Segment addresses are thentranslated into physical flash locations. Physical flash locations havean address range bounded by the amount of flash in the system inaccordance with some embodiments. Medium addresses and segment addressesare logical containers, and in some embodiments use a 128 bit or largeridentifier so as to be practically infinite, with a likelihood of reusecalculated as longer than the expected life of the system. Addressesfrom logical containers are allocated in a hierarchical fashion in someembodiments. Initially, each non-volatile solid state storage unit 152may be assigned a range of address space. Within this assigned range,the non-volatile solid state storage 152 is able to allocate addresseswithout synchronization with other non-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., a fieldprogrammable gate array (FPGA). In this embodiment, each flash die 222has pages, organized as sixteen kB (kilobyte) pages 224, and a register226 through which data can be written to or read from the flash die 222.In further embodiments, other types of solid-state memory are used inplace of, or in addition to flash memory illustrated within flash die222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The storage units 152described herein have multiple interfaces active simultaneously andserving multiple purposes. In some embodiments, some of thefunctionality of a storage node 150 is shifted into a storage unit 152,transforming the storage unit 152 into a combination of storage unit 152and storage node 150. Placing computing (relative to storage data) intothe storage unit 152 places this computing closer to the data itself.The various system embodiments have a hierarchy of storage node layerswith different capabilities. By contrast, in a storage array, acontroller owns and knows everything about all of the data that thecontroller manages in a shelf or storage devices. In a storage cluster161, as described herein, multiple controllers in multiple storage units152 and/or storage nodes 150 cooperate in various ways (e.g., forerasure coding, data sharding, metadata communication and redundancy,storage capacity expansion or contraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage units 152 of FIGS. 2A-C. In thisversion, each storage unit 152 has a processor such as controller 212(see FIG. 2C), an FPGA (field programmable gate array), flash memory206, and NVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS.2B and 2C) on a PCIe (peripheral component interconnect express) boardin a chassis 138 (see FIG. 2A). The storage unit 152 may be implementedas a single board containing storage, and may be the largest tolerablefailure domain inside the chassis. In some embodiments, up to twostorage units 152 may fail and the device will continue with no dataloss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the storage unit 152 DRAM 216, and is backed by NANDflash. NVRAM 204 is logically divided into multiple memory regionswritten for two as spool (e.g., spool_region). Space within the NVRAM204 spools is managed by each authority 168 independently. Each deviceprovides an amount of storage space to each authority 168. Thatauthority 168 further manages lifetimes and allocations within thatspace. Examples of a spool include distributed transactions or notions.When the primary power to a storage unit 152 fails, onboardsuper-capacitors provide a short duration of power hold up. During thisholdup interval, the contents of the NVRAM 204 are flushed to flashmemory 206. On the next power-on, the contents of the NVRAM 204 arerecovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources.

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g. partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some embodiments. Thus, whenan authority 168 migrates, the authority 168 continues to manage thesame storage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities′ 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or local area network (‘LAN’), or as some othermechanism capable of transporting digital information between thestorage system 306 and the cloud services provider 302. Such a datacommunications link 304 may be fully wired, fully wireless, or someaggregation of wired and wireless data communications pathways. In suchan example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using one or more data communications protocols.For example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using the handheld device transfer protocol(‘HDTP’), hypertext transfer protocol (‘HTTP’), internet protocol(‘IP’), real-time transfer protocol (‘RTP’), transmission controlprotocol (‘TCP’), user datagram protocol (‘UDP’), wireless applicationprotocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides services tousers of the cloud services provider 302 through the sharing ofcomputing resources via the data communications link 304. The cloudservices provider 302 may provide on-demand access to a shared pool ofconfigurable computing resources such as computer networks, servers,storage, applications and services, and so on. The shared pool ofconfigurable resources may be rapidly provisioned and released to a userof the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services to the storage system 306 and users ofthe storage system 306 through the implementation of an infrastructureas a service (‘IaaS’) service model where the cloud services provider302 offers computing infrastructure such as virtual machines and otherresources as a service to subscribers. In addition, the cloud servicesprovider 302 may be configured to provide services to the storage system306 and users of the storage system 306 through the implementation of aplatform as a service (‘PaaS’) service model where the cloud servicesprovider 302 offers a development environment to application developers.Such a development environment may include, for example, an operatingsystem, programming-language execution environment, database, webserver, or other components that may be utilized by applicationdevelopers to develop and run software solutions on a cloud platform.Furthermore, the cloud services provider 302 may be configured toprovide services to the storage system 306 and users of the storagesystem 306 through the implementation of a software as a service(‘SaaS’) service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. The cloud services provider302 may be further configured to provide services to the storage system306 and users of the storage system 306 through the implementation of anauthentication as a service (‘AaaS’) service model where the cloudservices provider 302 offers authentication services that can be used tosecure access to applications, data sources, or other resources. Thecloud services provider 302 may also be configured to provide servicesto the storage system 306 and users of the storage system 306 throughthe implementation of a storage as a service model where the cloudservices provider 302 offers access to its storage infrastructure foruse by the storage system 306 and users of the storage system 306.Readers will appreciate that the cloud services provider 302 may beconfigured to provide additional services to the storage system 306 andusers of the storage system 306 through the implementation of additionalservice models, as the service models described above are included onlyfor explanatory purposes and in no way represent a limitation of theservices that may be offered by the cloud services provider 302 or alimitation as to the service models that may be implemented by the cloudservices provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. Public cloud and private clouddeployment models may differ and may come with various advantages anddisadvantages. For example, because a public cloud deployment involvesthe sharing of a computing infrastructure across different organization,such a deployment may not be ideal for organizations with securityconcerns, mission-critical workloads, uptime requirements demands, andso on. While a private cloud deployment can address some of theseissues, a private cloud deployment may require on-premises staff tomanage the private cloud. In still alternative embodiments, the cloudservices provider 302 may be embodied as a mix of a private and publiccloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat additional hardware components and additional software componentsmay be necessary to facilitate the delivery of cloud services to thestorage system 306 and users of the storage system 306. For example, thestorage system 306 may be coupled to (or even include) a cloud storagegateway. Such a cloud storage gateway may be embodied, for example, ashardware-based or software-based appliance that is located on premiseswith the storage system 306. Such a cloud storage gateway may operate asa bridge between local applications that are executing on the storagearray 306 and remote, cloud-based storage that is utilized by thestorage array 306. Through the use of a cloud storage gateway,organizations may move primary iSCSI or NAS to the cloud servicesprovider 302, thereby enabling the organization to save space on theiron-premises storage systems. Such a cloud storage gateway may beconfigured to emulate a disk array, a block-based device, a file server,or other storage system that can translate the SCSI commands, fileserver commands, or other appropriate command into REST-space protocolsthat facilitate communications with the cloud services provider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. Such applications may takemany forms in accordance with various embodiments of the presentdisclosure. For example, the cloud services provider 302 may beconfigured to provide access to data analytics applications to thestorage system 306 and users of the storage system 306. Such dataanalytics applications may be configured, for example, to receivetelemetry data phoned home by the storage system 306. Such telemetrydata may describe various operating characteristics of the storagesystem 306 and may be analyzed, for example, to determine the health ofthe storage system 306, to identify workloads that are executing on thestorage system 306, to predict when the storage system 306 will run outof various resources, to recommend configuration changes, hardware orsoftware upgrades, workflow migrations, or other actions that mayimprove the operation of the storage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include storage resources308, which may be embodied in many forms. For example, in someembodiments the storage resources 308 can include nano-RAM or anotherform of nonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate. In some embodiments, the storage resources 308may include 3D crosspoint non-volatile memory in which bit storage isbased on a change of bulk resistance, in conjunction with a stackablecross-gridded data access array. In some embodiments, the storageresources 308 may include flash memory, including single-level cell(‘SLC’) NAND flash, multi-level cell (‘MLC’) NAND flash, triple-levelcell (‘TLC’) NAND flash, quad-level cell (‘QLC’) NAND flash, and others.In some embodiments, the storage resources 308 may include non-volatilemagnetoresistive random-access memory (‘MRAM’), including spin transfertorque (‘STT’) MRAM, in which data is stored through the use of magneticstorage elements. In some embodiments, the example storage resources 308may include non-volatile phase-change memory (‘PCM’) that may have theability to hold multiple bits in a single cell as cells can achieve anumber of distinct intermediary states. In some embodiments, the storageresources 308 may include quantum memory that allows for the storage andretrieval of photonic quantum information. In some embodiments, theexample storage resources 308 may include resistive random-access memory(‘ReRAM’) in which data is stored by changing the resistance across adielectric solid-state material. In some embodiments, the storageresources 308 may include storage class memory (‘SCM’) in whichsolid-state nonvolatile memory may be manufactured at a high densityusing some combination of sub-lithographic patterning techniques,multiple bits per cell, multiple layers of devices, and so on. Readerswill appreciate that other forms of computer memories and storagedevices may be utilized by the storage systems described above,including DRAM, SRAM, EEPROM, universal memory, and many others. Thestorage resources 308 depicted in FIG. 3A may be embodied in a varietyof form factors, including but not limited to, dual in-line memorymodules (‘DIMMs’), non-volatile dual in-line memory modules (‘NVDIMMs’),M.2, U.2, and others.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306. The communications resources 310may be configured to utilize a variety of different protocols and datacommunication fabrics to facilitate data communications betweencomponents within the storage systems as well as computing devices thatare outside of the storage system. For example, the communicationsresources 310 can include fibre channel (‘FC’) technologies such as FCfabrics and FC protocols that can transport SCSI commands over FCnetworks. The communications resources 310 can also include FC overethernet (‘FCoE’) technologies through which FC frames are encapsulatedand transmitted over Ethernet networks. The communications resources 310can also include InfiniBand (‘IB’) technologies in which a switchedfabric topology is utilized to facilitate transmissions between channeladapters. The communications resources 310 can also include NVM Express(‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’) technologiesthrough which non-volatile storage media attached via a PCI express(‘PCIe’) bus may be accessed. The communications resources 310 can alsoinclude mechanisms for accessing storage resources 308 within thestorage system 306 utilizing serial attached SCSI (‘SAS’), serial ATA(‘SATA’) bus interfaces for connecting storage resources 308 within thestorage system 306 to host bus adapters within the storage system 306,internet small computer systems interface (‘iSCSI’) technologies toprovide block-level access to storage resources 308 within the storagesystem 306, and other communications resources that that may be usefulin facilitating data communications between components within thestorage system 306, as well as data communications between the storagesystem 306 and computing devices that are outside of the storage system306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or moreapplication-specific integrated circuits (‘ASICs’) that are customizedfor some particular purpose as well as one or more central processingunits (‘CPUs’). The processing resources 312 may also include one ormore digital signal processors (‘DSPs’), one or more field-programmablegate arrays (‘FPGAs’), one or more systems on a chip (‘SoCs’), or otherform of processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform various tasks. The software resources314 may include, for example, one or more modules of computer programinstructions that when executed by processing resources 312 within thestorage system 306 are useful in carrying out various data protectiontechniques to preserve the integrity of data that is stored within thestorage systems. Readers will appreciate that such data protectiontechniques may be carried out, for example, by system software executingon computer hardware within the storage system, by a cloud servicesprovider, or in other ways. Such data protection techniques can include,for example, data archiving techniques that cause data that is no longeractively used to be moved to a separate storage device or separatestorage system for long-term retention, data backup techniques throughwhich data stored in the storage system may be copied and stored in adistinct location to avoid data loss in the event of equipment failureor some other form of catastrophe with the storage system, datareplication techniques through which data stored in the storage systemis replicated to another storage system such that the data may beaccessible via multiple storage systems, data snapshotting techniquesthrough which the state of data within the storage system is captured atvarious points in time, data and database cloning techniques throughwhich duplicate copies of data and databases may be created, and otherdata protection techniques. Through the use of such data protectiontechniques, business continuity and disaster recovery objectives may bemet as a failure of the storage system may not result in the loss ofdata stored in the storage system.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage resources 308 in the storage system 306. For example, thesoftware resources 314 may include software modules that perform carryout various data reduction techniques such as, for example, datacompression, data deduplication, and others. The software resources 314may include software modules that intelligently group together I/Ooperations to facilitate better usage of the underlying storage resource308, software modules that perform data migration operations to migratefrom within a storage system, as well as software modules that performother functions. Such software resources 314 may be embodied as one ormore software containers or in many other ways.

Readers will appreciate that the various components depicted in FIG. 3Bmay be grouped into one or more optimized computing packages asconverged infrastructures. Such converged infrastructures may includepools of computers, storage and networking resources that can be sharedby multiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may minimizecompatibility issues between various components within the storagesystem 306 while also reducing various costs associated with theestablishment and operation of the storage system 306. Such convergedinfrastructures may be implemented with a converged infrastructurereference architecture, with standalone appliances, with a softwaredriven hyper-converged approach (e.g., hyper-converged infrastructures),or in other ways.

Readers will appreciate that the storage system 306 depicted in FIG. 3Bmay be useful for supporting various types of software applications. Forexample, the storage system 306 may be useful in supporting artificialintelligence (‘AI’) applications, database applications, DevOpsprojects, electronic design automation tools, event-driven softwareapplications, high performance computing applications, simulationapplications, high-speed data capture and analysis applications, machinelearning applications, media production applications, media servingapplications, picture archiving and communication systems (‘PACS’)applications, software development applications, virtual realityapplications, augmented reality applications, and many other types ofapplications by providing storage resources to such applications.

The storage systems described above may operate to support a widevariety of applications. In view of the fact that the storage systemsinclude compute resources, storage resources, and a wide variety ofother resources, the storage systems may be well suited to supportapplications that are resource intensive such as, for example, AIapplications. Such AI applications may enable devices to perceive theirenvironment and take actions that maximize their chance of success atsome goal. Examples of such AI applications can include IBM Watson,Microsoft Oxford, Google DeepMind, Baidu Minwa, and others. The storagesystems described above may also be well suited to support other typesof applications that are resource intensive such as, for example,machine learning applications. Machine learning applications may performvarious types of data analysis to automate analytical model building.Using algorithms that iteratively learn from data, machine learningapplications can enable computers to learn without being explicitlyprogrammed.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. A GPU is a modern processorwith thousands of cores, well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks have ignited a new wave of algorithmsand tools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing, andmany others. Training deep neural networks, however, requires both highquality input data and large amounts of computation. GPUs are massivelyparallel processors capable of operating on large amounts of datasimultaneously. When combined into a multi-GPU cluster, a highthroughput pipeline may be required to feed input data from storage tothe compute engines. Deep learning is more than just constructing andtraining models. There also exists an entire data pipeline that must bedesigned for the scale, iteration, and experimentation necessary for adata science team to succeed.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

A data scientist works to improve the usefulness of the trained modelthrough a wide variety of approaches: more data, better data, smartertraining, and deeper models. In many cases, there will be teams of datascientists sharing the same datasets and working in parallel to producenew and improved training models. Often, there is a team of datascientists working within these phases concurrently on the same shareddatasets. Multiple, concurrent workloads of data processing,experimentation, and full-scale training layer the demands of multipleaccess patterns on the storage tier. In other words, storage cannot justsatisfy large file reads, but must contend with a mix of large and smallfile reads and writes. Finally, with multiple data scientists exploringdatasets and models, it may be critical to store data in its nativeformat to provide flexibility for each user to transform, clean, and usethe data in a unique way. The storage systems described above mayprovide a natural shared storage home for the dataset, with dataprotection redundancy (e.g., by using RAID6) and the performancenecessary to be a common access point for multiple developers andmultiple experiments. Using the storage systems described above mayavoid the need to carefully copy subsets of the data for local work,saving both engineering and GPU-accelerated servers use time. Thesecopies become a constant and growing tax as the raw data set and desiredtransformations constantly update and change.

Readers will appreciate that a fundamental reason why deep learning hasseen a surge in success is the continued improvement of models withlarger data set sizes. In contrast, classical machine learningalgorithms, like logistic regression, stop improving in accuracy atsmaller data set sizes. As such, the separation of compute resources andstorage resources may also allow independent scaling of each tier,avoiding many of the complexities inherent in managing both together. Asthe data set size grows or new data sets are considered, a scale outstorage system must be able to expand easily. Similarly, if moreconcurrent training is required, additional GPUs or other computeresources can be added without concern for their internal storage.Furthermore, the storage systems described above may make building,operating, and growing an AI system easier due to the random readbandwidth provided by the storage systems, the ability to of the storagesystems to randomly read small files (50 KB) high rates (meaning that noextra effort is required to aggregate individual data points to makelarger, storage-friendly files), the ability of the storage systems toscale capacity and performance as either the dataset grows or thethroughput requirements grow, the ability of the storage systems tosupport files or objects, the ability of the storage systems to tuneperformance for large or small files (i.e., no need for the user toprovision filesystems), the ability of the storage systems to supportnon-disruptive upgrades of hardware and software even during productionmodel training, and for many other reasons.

Small file performance of the storage tier may be critical as many typesof inputs, including text, audio, or images will be natively stored assmall files. If the storage tier does not handle small files well, anextra step will be required to pre-process and group samples into largerfiles. Storage, built on top of spinning disks, that relies on SSD as acaching tier, may fall short of the performance needed. Because trainingwith random input batches results in more accurate models, the entiredata set must be accessible with full performance. SSD caches onlyprovide high performance for a small subset of the data and will beineffective at hiding the latency of spinning drives.

Readers will appreciate that the storage systems described above may beconfigured to support the storage of (among of types of data)blockchains. Such blockchains may be embodied as a continuously growinglist of records, called blocks, which are linked and secured usingcryptography. Each block in a blockchain may contain a hash pointer as alink to a previous block, a timestamp, transaction data, and so on.Blockchains may be designed to be resistant to modification of the dataand can serve as an open, distributed ledger that can recordtransactions between two parties efficiently and in a verifiable andpermanent way. This makes blockchains potentially suitable for therecording of events, medical records, and other records managementactivities, such as identity management, transaction processing, andothers.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also possible.

Readers will appreciate that the systems described above may be bettersuited for the applications described above relative to other systemsthat may include, for example, a distributed direct-attached storage(DDAS) solution deployed in server nodes. Such DDAS solutions may bebuilt for handling large, less sequential accesses but may be less ableto handle small, random accesses. Readers will further appreciate thatthe storage systems described above may be utilized to provide aplatform for the applications described above that is preferable to theutilization of cloud-based resources as the storage systems may beincluded in an on-site or in-house infrastructure that is more secure,more locally and internally managed, more robust in feature sets andperformance, or otherwise preferable to the utilization of cloud-basedresources as part of a platform to support the applications describedabove. For example, services built on platforms such as IBM's Watson mayrequire a business enterprise to distribute individual user information,such as financial transaction information or identifiable patientrecords, to other institutions. As such, cloud-based offerings of AI asa service may be less desirable than internally managed and offered AIas a service that is supported by storage systems such as the storagesystems described above, for a wide array of technical reasons as wellas for various business reasons.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models.”

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. Likewise, machines likelocomotives and gas turbines that generate large amounts of informationthrough the use of a wide array of data-generating sensors may benefitfrom the rapid data processing capabilities of an edge solution. As anadditional example, some IoT devices such as connected video cameras maynot be well-suited for the utilization of cloud-based resources as itmay be impractical (not only from a privacy perspective, securityperspective, or a financial perspective) to send the data to the cloudsimply because of the pure volume of data that is involved. As such,many tasks that really on data processing, storage, or communicationsmay be better suited by platforms that include edge solutions such asthe storage systems described above.

Consider a specific example of inventory management in a warehouse,distribution center, or similar location. A large inventory,warehousing, shipping, order-fulfillment, manufacturing or otheroperation has a large amount of inventory on inventory shelves, and highresolution digital cameras that produce a firehose of large data. All ofthis data may be taken into an image processing system, which may reducethe amount of data to a firehose of small data. All of the small datamay be stored on-premises in storage. The on-premises storage, at theedge of the facility, may be coupled to the cloud, for external reports,real-time control and cloud storage. Inventory management may beperformed with the results of the image processing, so that inventorycan be tracked on the shelves and restocked, moved, shipped, modifiedwith new products, or discontinued/obsolescent products deleted, etc.The above scenario is a prime candidate for an embodiment of theconfigurable processing and storage systems described above. Acombination of compute-only blades and offload blades suited for theimage processing, perhaps with deep learning on offload-FPGA oroffload-custom blade(s) could take in the firehose of large data fromall of the digital cameras, and produce the firehose of small data. Allof the small data could then be stored by storage nodes, operating withstorage units in whichever combination of types of storage blades besthandles the data flow. This is an example of storage and functionacceleration and integration. Depending on external communication needswith the cloud, and external processing in the cloud, and depending onreliability of network connections and cloud resources, the system couldbe sized for storage and compute management with bursty workloads andvariable conductivity reliability. Also, depending on other inventorymanagement aspects, the system could be configured for scheduling andresource management in a hybrid edge/cloud environment.

The storage systems described above may also be optimized for use in bigdata analytics. Big data analytics may be generally described as theprocess of examining large and varied data sets to uncover hiddenpatterns, unknown correlations, market trends, customer preferences andother useful information that can help organizations make more-informedbusiness decisions. Big data analytics applications enable datascientists, predictive modelers, statisticians and other analyticsprofessionals to analyze growing volumes of structured transaction data,plus other forms of data that are often left untapped by conventionalbusiness intelligence (BI) and analytics programs. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form. Big data analytics is a form of advanced analytics,which involves complex applications with elements such as predictivemodels, statistical algorithms and what-if analyses powered byhigh-performance analytics systems.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa, Apple Siri, Google Voice, Samsung Bixby,Microsoft Cortana, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

For further explanation, FIG. 4 sets forth a block diagram illustratinga plurality of storage systems (402, 404, 406) that support a podaccording to some embodiments of the present disclosure. Althoughdepicted in less detail, the storage systems (402, 404, 406) depicted inFIG. 4 may be similar to the storage systems described above withreference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, or any combinationthereof. In fact, the storage systems (402, 404, 406) depicted in FIG. 4may include the same, fewer, or additional components as the storagesystems described above.

In the example depicted in FIG. 4 , each of the storage systems (402,404, 406) is depicted as having at least one computer processor (408,410, 412), computer memory (414, 416, 418), and computer storage (420,422, 424). Although in some embodiments the computer memory (414, 416,418) and the computer storage (420, 422, 424) may be part of the samehardware devices, in other embodiments the computer memory (414, 416,418) and the computer storage (420, 422, 424) may be part of differenthardware devices. The distinction between the computer memory (414, 416,418) and the computer storage (420, 422, 424) in this particular examplemay be that the computer memory (414, 416, 418) is physically proximateto the computer processors (408, 410, 412) and may store computerprogram instructions that are executed by the computer processors (408,410, 412), while the computer storage (420, 422, 424) is embodied asnon-volatile storage for storing user data, metadata describing the userdata, and so on. Referring to the example above in FIG. 1A, for example,the computer processors (408, 410, 412) and computer memory (414, 416,418) for a particular storage system (402, 404, 406) may reside withinone of more of the controllers (110A-110D) while the attached storagedevices (171A-171F) may serve as the computer storage (420, 422, 424)within a particular storage system (402, 404, 406).

In the example depicted in FIG. 4 , the depicted storage systems (402,404, 406) may attach to one or more pods (430, 432) according to someembodiments of the present disclosure. Each of the pods (430, 432)depicted in FIG. 4 can include a dataset (426, 428). For example, afirst pod (430) that three storage systems (402, 404, 406) have attachedto includes a first dataset (426) while a second pod (432) that twostorage systems (404, 406) have attached to includes a second dataset(428). In such an example, when a particular storage system attaches toa pod, the pod's dataset is copied to the particular storage system andthen kept up to date as the dataset is modified. Storage systems can beremoved from a pod, resulting in the dataset being no longer kept up todate on the removed storage system. In the example depicted in FIG. 4 ,any storage system which is active for a pod (it is an up-to-date,operating, non-faulted member of a non-faulted pod) can receive andprocess requests to modify or read the pod's dataset.

In the example depicted in FIG. 4 , each pod (430, 432) may also includea set of managed objects and management operations, as well as a set ofaccess operations to modify or read the dataset (426, 428) that isassociated with the particular pod (430, 432). In such an example, themanagement operations may modify or query managed objects equivalentlythrough any of the storage systems. Likewise, access operations to reador modify the dataset may operate equivalently through any of thestorage systems. In such an example, while each storage system stores aseparate copy of the dataset as a proper subset of the datasets storedand advertised for use by the storage system, the operations to modifymanaged objects or the dataset performed and completed through any onestorage system are reflected in subsequent management objects to querythe pod or subsequent access operations to read the dataset.

Readers will appreciate that pods may implement more capabilities thanjust a clustered synchronously replicated dataset. For example, pods canbe used to implement tenants, whereby datasets are in some way securelyisolated from each other. Pods can also be used to implement virtualarrays or virtual storage systems where each pod is presented as aunique storage entity on a network (e.g., a Storage Area Network, orInternet Protocol network) with separate addresses. In the case of amulti-storage-system pod implementing a virtual storage system, allphysical storage systems associated with the pod may present themselvesas in some way the same storage system (e.g., as if the multiplephysical storage systems were no different than multiple network portsinto a single storage system).

Readers will appreciate that pods may also be units of administration,representing a collection of volumes, file systems, object/analyticstores, snapshots, and other administrative entities, where makingadministrative changes (e.g., name changes, property changes, managingexports or permissions for some part of the pod's dataset), on any onestorage system is automatically reflected to all active storage systemsassociated with the pod. In addition, pods could also be units of datacollection and data analysis, where performance and capacity metrics arepresented in ways that aggregate across all active storage systems forthe pod, or that call out data collection and analysis separately foreach pod, or perhaps presenting each attached storage system'scontribution to the incoming content and performance for each a pod.

One model for pod membership may be defined as a list of storagesystems, and a subset of that list where storage systems are consideredto be in-sync for the pod. A storage system may be considered to bein-sync for a pod if it is at least within a recovery of havingidentical idle content for the last written copy of the datasetassociated with the pod. Idle content is the content after anyin-progress modifications have completed with no processing of newmodifications. Sometimes this is referred to as “crash recoverable”consistency. Recovery of a pod carries out the process of reconcilingdifferences in applying concurrent updates to in-sync storage systems inthe pod. Recovery can resolve any inconsistencies between storagesystems in the completion of concurrent modifications that had beenrequested to various members of the pod but that were not signaled toany requestor as having completed successfully. Storage systems that arelisted as pod members but that are not listed as in-sync for the pod canbe described as “detached” from the pod. Storage systems that are listedas pod members, are in-sync for the pod, and are currently available foractively serving data for the pod are “online” for the pod.

Each storage system member of a pod may have its own copy of themembership, including which storage systems it last knew were in-sync,and which storage systems it last knew comprised the entire set of podmembers. To be online for a pod, a storage system must consider itselfto be in-sync for the pod and must be communicating with all otherstorage systems it considers to be in-sync for the pod. If a storagesystem can't be certain that it is in-sync and communicating with allother storage systems that are in-sync, then it must stop processing newincoming requests for the pod (or must complete them with an error orexception) until it can be certain that it is in-sync and communicatingwith all other storage systems that are in-sync. A first storage systemmay conclude that a second paired storage system should be detached,which will allow the first storage system to continue since it is nowin-sync with all storage systems now in the list. But, the secondstorage system must be prevented from concluding, alternatively, thatthe first storage system should be detached and with the second storagesystem continuing operation. This would result in a “split brain”condition that can lead to irreconcilable datasets, dataset corruption,or application corruption, among other dangers.

The situation of needing to determine how to proceed when notcommunicating with paired storage systems can arise while a storagesystem is running normally and then notices lost communications, whileit is currently recovering from some previous fault, while it isrebooting or resuming from a temporary power loss or recoveredcommunication outage, while it is switching operations from one set ofstorage system controller to another set for whatever reason, or duringor after any combination of these or other kinds of events. In fact, anytime a storage system that is associated with a pod can't communicatewith all known non-detached members, the storage system can either waitbriefly until communications can be established, go offline and continuewaiting, or it can determine through some means that it is safe todetach the non-communicating storage system without risk of incurring asplit brain due to the non-communicating storage system concluding thealternative view, and then continue. If a safe detach can happen quicklyenough, the storage system can remain online for the pod with littlemore than a short delay and with no resulting application outages forapplications that can issue requests to the remaining online storagesystems.

One example of this situation is when a storage system may know that itis out-of-date. That can happen, for example, when a first storagesystem is first added to a pod that is already associated with one ormore storage systems, or when a first storage system reconnects toanother storage system and finds that the other storage system hadalready marked the first storage system as detached. In this case, thisfirst storage system will simply wait until it connects to some otherset of storage systems that are in-sync for the pod.

This model demands some degree of consideration for how storage systemsare added to or removed from pods or from the in-sync pod members list.Since each storage system will have its own copy of the list, and sincetwo independent storage systems can't update their local copy at exactlythe same time, and since the local copy is all that is available on areboot or in various fault scenarios, care must be taken to ensure thattransient inconsistencies don't cause problems. For example, if onestorage systems is in-sync for a pod and a second storage system isadded, then if the second storage system is updated to list both storagesystems as in-sync first, then if there is a fault and a restart of bothstorage systems, the second might startup and wait to connect to thefirst storage system while the first might be unaware that it should orcould wait for the second storage system. If the second storage systemthen responds to an inability to connect with the first storage systemby going through a process to detach it, then it might succeed incompleting a process that the first storage system is unaware of,resulting in a split brain. As such, it may be necessary to ensure thatstorage systems won't disagree inappropriately on whether they might optto go through a detach process if they aren't communicating.

One way to ensure that storage systems won't disagree inappropriately onwhether they might opt to go through a detach process if they aren'tcommunicating is to ensure that when adding a new storage system to thein-sync member list for a pod, the new storage system first stores thatit is a detached member (and perhaps that it is being added as anin-sync member). Then, the existing in-sync storage systems can locallystore that the new storage system is an in-sync pod member before thenew storage system locally stores that same fact. If there is a set ofreboots or network outages prior to the new storage system storing itsin-sync status, then the original storage systems may detach the newstorage system due to non-communication, but the new storage system willwait. A reverse version of this change might be needed for removing acommunicating storage system from a pod: first the storage system beingremoved stores that it is no longer in-sync, then the storage systemsthat will remain store that the storage system being removed is nolonger in-sync, then all storage systems delete the storage system beingremoved from their pod membership lists. Depending on theimplementation, an intermediate persisted detached state may not benecessary. Whether or not care is required in local copies of membershiplists may depend on the model storage systems use for monitoring eachother or for validating their membership. If a consensus model is usedfor both, or if an external system (or an external distributed orclustered system) is used to store and validate pod membership, theninconsistencies in locally stored membership lists may not matter.

When communications fail or one or several storage systems in a podfail, or when a storage system starts up (or fails over to a secondarycontroller) and can't communicate with paired storage systems for a pod,and it is time for one or more storage systems to decide to detach oneor more paired storage systems, some algorithm or mechanism must beemployed to decide that it is safe to do so and to follow through on thedetach. One means of resolving detaches is use a majority (or quorum)model for membership. With three storage systems, as long as two arecommunicating, they can agree to detach a third storage system thatisn't communicating, but that third storage system cannot by itselfchoose to detach either of the other two. Confusion can arise whenstorage system communication is inconsistent. For example, storagesystem A might be communicating with storage system B but not C, whilestorage system B might be communicating with both A and C. So, A and Bcould detach C, or B and C could detach A, but more communicationbetween pod members may be needed to figure this out.

Care needs to be taken in a quorum membership model when adding andremoving storage systems. For example, if a fourth storage system isadded, then a “majority” of storage systems is at that point three. Thetransition from three storage systems (with two required for majority)to a pod including a fourth storage system (with three required formajority) may require something similar to the model describedpreviously for carefully adding a storage system to the in-sync list.For example, the fourth storage system might start in an attaching statebut not yet attached where it would never instigate a vote over quorum.Once in that state, the original three pod members could each be updatedto be aware of the fourth member and the new requirement for a threestorage system majority to detach a fourth. Removing a storage systemfrom a pod might similarly move that storage system to a locally stored“detaching” state before updating other pod members. A variant schemefor this is to use a distributed consensus mechanism such as PAXOS orRAFT to implement any membership changes or to process detach requests.

Another means of managing membership transitions is to use an externalsystem that is outside of the storage systems themselves to handle podmembership. In order to become online for a pod, a storage system mustfirst contact the external pod membership system to verify that it isin-sync for the pod. Any storage system that is online for a pod shouldthen remain in communication with the pod membership system and shouldwait or go offline if it loses communication. An external pod membershipmanager could be implemented as a highly available cluster using variouscluster tools, such as Oracle RAC, Linux HA, VERITAS Cluster Server,IBM's HACMP, or others. An external pod membership manager could alsouse distributed configuration tools such as Etcd or Zookeeper, or areliable distributed database such as Amazon's DynamoDB.

In the example depicted in FIG. 4 , the depicted storage systems (402,404, 406) may receive a request to read a portion of the dataset (426,428) and process the request to read the portion of the dataset locallyaccording to some embodiments of the present disclosure. Readers willappreciate that although requests to modify (e.g., a write operation)the dataset (426, 428) require coordination between the storage systems(402, 404, 406) in a pod, as the dataset (426, 428) should be consistentacross all storage systems (402, 404, 406) in a pod, responding to arequest to read a portion of the dataset (426, 428) does not requiresimilar coordination between the storage systems (402, 404, 406). Assuch, a particular storage system that receives a read request mayservice the read request locally by reading a portion of the dataset(426, 428) that is stored within the storage system's storage devices,with no synchronous communication with other storage systems in the pod.Read requests received by one storage system for a replicated dataset ina replicated cluster are expected to avoid any communication in the vastmajority of cases, at least when received by a storage system that isrunning within a cluster that is also running nominally. Such readsshould normally be processed simply by reading from the local copy of aclustered dataset with no further interaction required with otherstorage systems in the cluster.

Readers will appreciate that the storage systems may take steps toensure read consistency such that a read request will return the sameresult regardless of which storage system processes the read request.For example, the resulting clustered dataset content for any set ofupdates received by any set of storage systems in the cluster should beconsistent across the cluster, at least at any time updates are idle(all previous modifying operations have been indicated as complete andno new update requests have been received and processed in any way).More specifically, the instances of a clustered dataset across a set ofstorage systems can differ only as a result of updates that have not yetcompleted. This means, for example, that any two write requests whichoverlap in their volume block range, or any combination of a writerequest and an overlapping snapshot, compare-and-write, or virtual blockrange copy, must yield a consistent result on all copies of the dataset.Two operations should not yield a result as if they happened in oneorder on one storage system and a different order on another storagesystem in the replicated cluster.

Furthermore, read requests can be made time order consistent. Forexample, if one read request is received on a replicated cluster andcompleted and that read is then followed by another read request to anoverlapping address range which is received by the replicated clusterand where one or both reads in any way overlap in time and volumeaddress range with a modification request received by the replicatedcluster (whether any of the reads or the modification are received bythe same storage system or a different storage system in the replicatedcluster), then if the first read reflects the result of the update thenthe second read should also reflect the results of that update, ratherthan possibly returning data that preceded the update. If the first readdoes not reflect the update, then the second read can either reflect theupdate or not. This ensures that between two read requests “time” for adata segment cannot roll backward.

In the example depicted in FIG. 4 , the depicted storage systems (402,404, 406) may also detect a disruption in data communications with oneor more of the other storage systems and determine whether to theparticular storage system should remain in the pod. A disruption in datacommunications with one or more of the other storage systems may occurfor a variety of reasons. For example, a disruption in datacommunications with one or more of the other storage systems may occurbecause one of the storage systems has failed, because a networkinterconnect has failed, or for some other reason. An important aspectof synchronous replicated clustering is ensuring that any fault handlingdoesn't result in unrecoverable inconsistencies, or any inconsistency inresponses. For example, if a network fails between two storage systems,at most one of the storage systems can continue processing newlyincoming I/O requests for a pod. And, if one storage system continuesprocessing, the other storage system can't process any new requests tocompletion, including read requests.

In the example depicted in FIG. 4 , the depicted storage systems (402,404, 406) may also determine whether the particular storage systemshould remain in the pod in response to detecting a disruption in datacommunications with one or more of the other storage systems. Asmentioned above, to be ‘online’ as part of a pod, a storage system mustconsider itself to be in-sync for the pod and must be communicating withall other storage systems it considers to be in-sync for the pod. If astorage system can't be certain that it is in-sync and communicatingwith all other storage systems that are in-sync, then it may stopprocessing new incoming requests to access the dataset (426, 428). Assuch, the storage system may determine whether to the particular storagesystem should remain online as part of the pod, for example, bydetermining whether it can communicate with all other storage systems itconsiders to be in-sync for the pod (e.g., via one or more testmessages), by determining whether the all other storage systems itconsiders to be in-sync for the pod also consider the storage system tobe attached to the pod, through a combination of both steps where theparticular storage system must confirm that it can communicate with allother storage systems it considers to be in-sync for the pod and thatall other storage systems it considers to be in-sync for the pod alsoconsider the storage system to be attached to the pod, or through someother mechanism.

In the example depicted in FIG. 4 , the depicted storage systems (402,404, 406) may also keep the dataset on the particular storage systemaccessible for management and dataset operations in response todetermining that the particular storage system should remain in the pod.The storage system may keep the dataset (426, 428) on the particularstorage system accessible for management and dataset operations, forexample, by accepting requests to access the version of the dataset(426, 428) that is stored on the storage system and processing suchrequests, by accepting and processing management operations associatedwith the dataset (426, 428) that are issued by a host or authorizedadministrator, by accepting and processing management operationsassociated with the dataset (426, 428) that are issued by one of theother storage systems, or in some other way.

In the example depicted in FIG. 4 , the depicted storage systems (402,404, 406) may, however, make the dataset on the particular storagesystem inaccessible for management and dataset operations in response todetermining that the particular storage system should not remain in thepod. The storage system may make the dataset (426, 428) on theparticular storage system inaccessible for management and datasetoperations, for example, by rejecting requests to access the version ofthe dataset (426, 428) that is stored on the storage system, byrejecting management operations associated with the dataset (426, 428)that are issued by a host or other authorized administrator, byrejecting management operations associated with the dataset (426, 428)that are issued by one of the other storage systems in the pod, or insome other way.

In the example depicted in FIG. 4 , the depicted storage systems (402,404, 406) may also detect that the disruption in data communicationswith one or more of the other storage systems has been repaired and makethe dataset on the particular storage system accessible for managementand dataset operations. The storage system may detect that thedisruption in data communications with one or more of the other storagesystems has been repaired, for example, by receiving a message from theone or more of the other storage systems. In response to detecting thatthe disruption in data communications with one or more of the otherstorage systems has been repaired, the storage system may make thedataset (426, 428) on the particular storage system accessible formanagement and dataset operations once the previously detached storagesystem has been resynchronized with the storage systems that remainedattached to the pod.

In the example depicted in FIG. 4 , the depicted storage systems (402,404, 406) may also go offline from the pod such that the particularstorage system no longer allows management and dataset operations. Thedepicted storage systems (402, 404, 406) may go offline from the podsuch that the particular storage system no longer allows management anddataset operations for a variety of reasons. For example, the depictedstorage systems (402, 404, 406) may also go offline from the pod due tosome fault with the storage system itself, because an update or someother maintenance is occurring on the storage system, due tocommunications faults, or for many other reasons. In such an example,the depicted storage systems (402, 404, 406) may subsequently update thedataset on the particular storage system to include all updates to thedataset since the particular storage system went offline and go backonline with the pod such that the particular storage system allowsmanagement and dataset operations, as will be described in greaterdetail in the resynchronization sections included below.

In the example depicted in FIG. 4 , the depicted storage systems (402,404, 406) may also identifying a target storage system forasynchronously receiving the dataset, where the target storage system isnot one of the plurality of storage systems across which the dataset issynchronously replicated. Such a target storage system may represent,for example, a backup storage system, as some storage system that makesuse of the synchronously replicated dataset, and so on. In fact,synchronous replication can be leveraged to distribute copies of adataset closer to some rack of servers, for better local readperformance. One such case is smaller top-of-rack storage systemssymmetrically replicated to larger storage systems that are centrallylocated in the data center or campus and where those larger storagesystems are more carefully managed for reliability or are connected toexternal networks for asynchronous replication or backup services.

In the example depicted in FIG. 4 , the depicted storage systems (402,404, 406) may also identify a portion of the dataset that is not beingasynchronously replicated to the target storage system by any of theother storages systems and asynchronously replicate, to the targetstorage system, the portion of the dataset that is not beingasynchronously replicated to the target storage system by any of theother storages systems, wherein the two or more storage systemscollectively replicate the entire dataset to the target storage system.In such a way, the work associated with asynchronously replicating aparticular dataset may be split amongst the members of a pod, such thateach storage system in a pod is only responsible for asynchronouslyreplicating a subset of a dataset to the target storage system.

In the example depicted in FIG. 4 , the depicted storage systems (402,404, 406) may also detach from the pod, such that the particular storagesystem that detaches from the pod is no longer included in the set ofstorage systems across which the dataset is synchronously replicated.For example, if storage system (404) in FIG. 4 detached from the pod(430) illustrated in FIG. 4 , the pod (430) would only include storagesystems (402, 406) as the storage systems across which the dataset (426)that is included in the pod (430) would be synchronously replicatedacross. In such an example, detaching the storage system from the podcould also include removing the dataset from the particular storagesystem that detached from the pod. Continuing with the example where thestorage system (404) in FIG. 4 detached from the pod (430) illustratedin FIG. 4 , the dataset (426) that is included in the pod (430) could bedeleted or otherwise removed from the storage system (404).

Readers will appreciate that there are a number of unique administrativecapabilities enabled by the pod model that can further be supported.Also, the pod model itself introduces some issues that can be addressedby an implementation. For example, when a storage system is offline fora pod, but is otherwise running, such as because an interconnect failedand another storage system for the pod won out in mediation, there maystill be a desire or need to access the offline pod's dataset on theoffline storage system. One solution may be simply to enable the pod insome detached mode and allow the dataset to be accessed. However, thatsolution can be dangerous and that solution can cause the pod's metadataand data to be much more difficult to reconcile when the storage systemsdo regain communication. Furthermore, there could still be a separatepath for hosts to access the offline storage system as well as the stillonline storage systems. In that case, a host might issue I/O to bothstorage systems even though they are no longer being kept in sync,because the host sees target ports reporting volumes with the sameidentifiers and the host I/O drivers presume it sees additional paths tothe same volume. This can result in fairly damaging data corruption asreads and writes issued to both storage systems are no longer consistenteven though the host presumes they are. As a variant of this case, in aclustered application, such as a shared storage clustered database, theclustered application running on one host might be reading or writing toone storage system and the same clustered application running on anotherhost might be reading or writing to the “detached” storage system, yetthe two instances of the clustered application are communicating betweeneach other on the presumption that the dataset they each see is entirelyconsistent for completed writes. Since they aren't consistent, thatpresumption is violated and the application's dataset (e.g., thedatabase) can quickly end up being corrupted.

One way to solve both of these problems is to allow for an offline pod,or perhaps a snapshot of an offline pod, to be copied to a new pod withnew volumes that have sufficiently new identities that host I/O driversand clustered applications won't confuse the copied volumes as being thesame as the still online volumes on another storage system. Since eachpod maintains a complete copy of the dataset, which is crash consistentbut perhaps slightly different from the copy of the pod dataset onanother storage system, and since each pod has an independent copy ofall data and metadata needed to operate on the pod content, it is astraightforward problem to make a virtual copy of some or all volumes orsnapshots in the pod to new volumes in a new pod. In a logical extentgraph implementation, for example, all that is needed is to define newvolumes in a new pod which reference logical extent graphs from thecopied pod associated with the pod's volumes or snapshots, and with thelogical extent graphs being marked as copy on write. The new volumesshould be treated as new volumes, similarly to how volume snapshotscopied to a new volume might be implemented. Volumes may have the sameadministrative name, though within a new pod namespace. But, they shouldhave different underlying identifiers, and differing logical unitidentifiers from the original volumes.

In some cases it may be possible to use virtual network isolationtechniques (for example, by creating a virtual LAN in the case of IPnetworks or a virtual SAN in the case of fiber channel networks) in sucha way that isolation of volumes presented to some interfaces can beassured to be inaccessible from host network interfaces or host SCSIinitiator ports that might also see the original volumes. In such cases,it may be safe to provide the copies of volumes with the same SCSI orother storage identifiers as the original volumes. This could be used,for example, in cases where the applications expect to see a particularset of storage identifiers in order to function without an undue burdenin reconfiguration.

Some of the techniques described herein could also be used outside of anactive fault context to test readiness for handling faults. Readinesstesting (sometimes referred to as “fire drills”) is commonly requiredfor disaster recovery configurations, where frequent and repeatedtesting is considered a necessity to ensure that most or all aspects ofa disaster recovery plan are correct and account for any recent changesto applications, datasets, or changes in equipment. Readiness testingshould be non-disruptive to current production operations, includingreplication. In many cases the real operations can't actually be invokedon the active configuration, but a good way to get close is to usestorage operations to make copies of production datasets, and thenperhaps couple that with the use of virtual networking, to create anisolated environment containing all data that is believed necessary forthe important applications that must be brought up successfully in casesof disasters. Making such a copy of a synchronously replicated (or evenan asynchronously replicated) dataset available within a site (orcollection of sites) that is expected to perform a disaster recoveryreadiness test procedure and then starting the important applications onthat dataset to ensure that it can startup and function is a great tool,since it helps ensure that no important parts of the applicationdatasets were left out in the disaster recovery plan. If necessary, andpractical, this could be coupled with virtual isolated networks coupledperhaps with isolated collection of physical or virtual machines, to getas close as possible to a real world disaster recovery takeoverscenario. Virtually copying a pod (or set of pods) to another pod as apoint-in-time image of the pod datasets immediately creates an isolateddataset that contains all the copied elements and that can then beoperated on essentially identically to the originally pods, as well asallowing isolation to a single site (or a few sites) separately from theoriginal pod. Further, these are fast operations and they can be torndown and repeated easily allowing testing to repeated as often as isdesired.

Some enhancements could be made to get further toward perfect disasterrecovery testing. For example, in conjunction with isolated networks,SCSI logical unit identities or other types of identities could becopied into the target pod so that the test servers, virtual machines,and applications see the same identities. Further, the administrativeenvironment of the servers could be configured to respond to requestsfrom a particular virtual set of virtual networks to respond to requestsand operations on the original pod name so scripts don't require use oftest-variants with alternate “test” versions of object names. A furtherenhancement can be used in cases where the host-side serverinfrastructure that will take over in the case of a disaster takeovercan be used during a test. This includes cases where a disaster recoverydata center is completely stocked with alternative server infrastructurethat won't generally be used until directed to do so by a disaster. Italso includes cases where that infrastructure might be used fornon-critical operations (for example, running analytics on productiondata, or simply supporting application development or other functionswhich may be important but can be halted if needed for more criticalfunctions). Specifically, host definitions and configurations and theserver infrastructure that will use them can be set up as they will befor an actual disaster recovery takeover event and tested as part ofdisaster recovery takeover testing, with the tested volumes beingconnected to these host definitions from the virtual pod copy used toprovide a snapshot of the dataset. From the standpoint of the storagesystems involved, then, these host definitions and configurations usedfor testing, and the volume-to-host connection configurations usedduring testing, can be reused when an actual disaster takeover event istriggered, greatly minimizing the configuration differences between thetest configuration and the real configuration that will be used in caseof a disaster recovery takeover.

In some cases it may make sense to move volumes out of a first pod andinto a new second pod including just those volumes. The pod membershipand high availability and recovery characteristics can then be adjustedseparately, and administration of the two resulting pod datasets canthen be isolated from each other. An operation that can be done in onedirection should also be possible in the other direction. At some point,it may make sense to take two pods and merge them into one so that thevolumes in each of the original two pods will now track each other forstorage system membership and high availability and recoverycharacteristics and events. Both operations can be accomplished safelyand with reasonably minimal or no disruption to running applications byrelying on the characteristics suggested for changing mediation orquorum properties for a pod which were discussed in an earlier section.With mediation, for example, a mediator for a pod can be changed using asequence consisting of a step where each storage system in a pod ischanged to depend on both a first mediator and a second mediator andeach is then changed to depend only on the second mediator. If a faultoccurs in the middle of the sequence, some storage systems may depend onboth the first mediator and the second mediator, but in no case willrecovery and fault handling result in some storage systems dependingonly on the first mediator and other storage systems only depending onthe second mediator. Quorum can be handled similarly by temporarilydepending on winning against both a first quorum model and a secondquorum model in order to proceed to recovery. This may result in a veryshort time period where availability of the pod in the face of faultsdepend on additional resources, thus reducing potential availability,but this time period is very short and the reduction in availability isoften very little. With mediation, if the change in mediator parametersis nothing more than the change in the key used for mediation and themediation service used is the same, then the potential reduction inavailability is even less, since it now depends only on two calls to thesame service versus one call to that service, and rather than separatecalls to two separate services.

Readers will note that changing the quorum model may be quite complex.An additional step may be necessary where storage systems willparticipate in the second quorum model but won't depend on winning inthat second quorum model, which is then followed by the step of alsodepending on the second quorum model. This may be necessary to accountfor the fact that if only one system has processed the change to dependon the quorum model, then it will never win quorum since there willnever be a majority. With this model in place for changing the highavailability parameters (mediation relationship, quorum model, takeoverpreferences), we can create a safe procedure for these operations tosplit a pod into two or to join two pods into one. This may requireadding one other capability: linking a second pod to a first pod forhigh availability such that if two pods include compatible highavailability parameters the second pod linked to the first pod candepend on the first pod for determining and instigating detach-relatedprocessing and operations, offline and in-sync states, and recovery andresynchronization actions.

To split a pod into two, which is an operation to move some volumes intoa newly created pod, a distributed operation may be formed that can bedescribed as: form a second pod into which we will move a set of volumeswhich were previously in a first pod, copy the high availabilityparameters from the first pod into the second pod to ensure they arecompatible for linking, and link the second pod to the first pod forhigh availability. This operation may be encoded as messages and shouldbe implemented by each storage system in the pod in such a way that thestorage system ensures that the operation happens completely on thatstorage system or does not happen at all if processing is interrupted bya fault. Once all in-sync storage systems for the two pods haveprocessed this operation, the storage systems can then process asubsequent operation which changes the second pod so that it is nolonger linked to the first pod. As with other changes to highavailability characteristics for a pod, this involves first having eachin-sync storage system change to rely on both the previous model (thatmodel being that high availability is linked to the first pod) and thenew model (that model being its own now independent high availability).In the case of mediation or quorum, this means that storage systemswhich processed this change will first depend on mediation or quorumbeing achieved as appropriate for the first pod and will additionallydepend on a new separate mediation (for example, a new mediation key) orquorum being achieved for the second pod before the second pod canproceed following a fault that required mediation or testing for quorum.As with the previous description of changing quorum models, anintermediate step may set storage systems to participate in quorum forthe second pod before the step where storage systems participate in anddepend on quorum for the second pod. Once all in-sync storage systemshave processed the change to depend on the new parameters for mediationor quorum for both the first pod and the second pod, the split iscomplete.

Joining a second pod into a first pod operates essentially in reverse.First, the second pod must be adjusted to be compatible with the firstpod, by having an identical list of storage systems and by having acompatible high availability model. This may involve some set of stepssuch as those described elsewhere in this paper to add or remove storagesystems or to change mediator and quorum models. Depending onimplementation, it may be necessary only to reach an identical list ofstorage systems. Joining proceeds by processing an operation on eachin-sync storage system to link the second pod to the first pod for highavailability. Each storage system which processes that operation willthen depend on the first pod for high availability and then the secondpod for high availability. Once all in-sync storage systems for thesecond pod have processed that operation, the storage systems will theneach process a subsequent operation to eliminate the link between thesecond pod and the first pod, migrate the volumes from the second podinto the first pod, and delete the second pod. Host or applicationdataset access can be preserved throughout these operations, as long asthe implementation allows proper direction of host or applicationdataset modification or read operations to the volume by identity and aslong as the identity is preserved as appropriate to the storage protocolor storage model (for example, as long as logical unit identifiers forvolumes and use of target ports for accessing volumes are preserved inthe case of SCSI).

Migrating a volume between pods may present issues. If the pods have anidentical set of in-sync membership storage systems, then it may bestraightforward: temporarily suspend operations on the volumes beingmigrated, switch control over operations on those volumes to controllingsoftware and structures for the new pod, and then resume operations.This allows for a seamless migration with continuous uptime forapplications apart from the very brief operation suspension, providednetwork and ports migrate properly between pods. Depending on theimplementation, suspending operations may not even be necessary, or maybe so internal to the system that the suspension of operations has noimpact. Copying volumes between pods with different in-sync membershipsets is more of a problem. If the target pod for the copy has a subsetof in-sync members from the source pod, this isn't much of a problem: amember storage system can be dropped safely enough without having to domore work. But, if the target pod adds in-sync member storage systems tothe volume over the source pod, then the added storage systems must besynchronized to include the volume's content before they can be used.Until synchronized, this leaves the copied volumes distinctly differentfrom the already synchronized volumes, in that fault handling differsand request handling from the not yet synced member storage systemseither won't work or must be forwarded or won't be as fast because readswill have to traverse an interconnect. Also, the internal implementationwill have to handle some volumes being in sync and ready for faulthandling and others not being in sync.

There are other problems relating to reliability of the operation in theface of faults. Coordinating a migration of volumes betweenmulti-storage-system pods is a distributed operation. If pods are theunit of fault handling and recovery, and if mediation or quorum orwhatever means are used to avoid split-brain situations, then a switchin volumes from one pod with a particular set of state andconfigurations and relationships for fault handling, recovery, mediationand quorum to another then storage systems in a pod have to be carefulabout coordinating changes related to that handling for any volumes.Operations can't be atomically distributed between storage systems, butmust be staged in some way. Mediation and quorum models essentiallyprovide pods with the tools for implementing distributed transactionalatomicity, but this may not extend to inter-pod operations withoutadding to the implementation.

Consider even a simple migration of a volume from a first pod to asecond pod even for two pods that share the same first and secondstorage systems. At some point the storage systems will coordinate todefine that the volume is now in the second pod and is no longer in thefirst pod. If there is no inherent mechanism for transactional atomicityacross the storage systems for the two pods, then a naive implementationcould leave the volume in the first pod on the first storage system andthe second pod on the second storage system at the time of a networkfault that results in fault handling to detach storage systems from thetwo pods. If pods separately determine which storage system succeeds indetaching the other, then the result could be that the same storagesystem detaches the other storage system for both pods, in which casethe result of the volume migration recovery should be consistent, or itcould result in a different storage system detaching the other for thetwo pods. If the first storage system detaches the second storage systemfor the first pod and the second storage system detaches the firststorage system for the second pod, then recovery might result in thevolume being recovered to the first pod on the first storage system andinto the second pod on the second storage system, with the volume thenrunning and exported to hosts and storage applications on both storagesystems. If instead the second storage system detaches the first storagesystem for the first pod and first storage detaches the second storagesystem for the second pod, then recovery might result in the volumebeing discarded from the second pod by the first storage system and thevolume being discarded from the first pod by the second storage system,resulting in the volume disappearing entirely. If the pods a volume isbeing migrated between are on differing sets of storage systems, thenthings can get even more complicated.

A solution to these problems may be to use an intermediate pod alongwith the techniques described previously for splitting and joining pods.This intermediate pod may never be presented as visible managed objectsassociated with the storage systems. In this model, volumes to be movedfrom a first pod to a second pod are first split from the first pod intoa new intermediate pod using the split operation described previously.The storage system members for the intermediate pod can then be adjustedto match the membership of storage systems by adding or removing storagesystems from the pod as necessary. Subsequently, the intermediate podcan be joined with the second pod.

For further explanation, FIG. 5 sets forth a flow chart illustratingsteps that may be performed by storage systems (402, 404, 406) thatsupport a pod according to some embodiments of the present disclosure.Although depicted in less detail, the storage systems (402. 404, 406)depicted in FIG. 5 may be similar to the storage systems described abovewith reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, FIG. 4 , or anycombination thereof. In fact, the storage systems (402, 404, 406)depicted in FIG. 5 may include the same, fewer, additional components asthe storage systems described above.

In the example method depicted in FIG. 5 , a storage system (402) mayattach (508) to a pod. The model for pod membership may include a listof storage systems and a subset of that list where storage systems arepresumed to be in-sync for the pod. A storage system is in-sync for apod if it is at least within a recovery of having identical idle contentfor the last written copy of the dataset associated with the pod. Idlecontent is the content after any in-progress modifications havecompleted with no processing of new modifications. Sometimes this isreferred to as “crash recoverable” consistency. Storage systems that arelisted as pod members but that are not listed as in-sync for the pod canbe described as “detached” from the pod. Storage systems that are listedas pod members, are in-sync for the pod, and are currently available foractively serving data for the pod are “online” for the pod.

In the example method depicted in FIG. 5 , the storage system (402) mayattach (508) to a pod, for example, by synchronizing its locally storedversion of the dataset (426) along with an up-to-date version of thedataset (426) that is stored on other storage systems (404, 406) in thepod that are online, as the term is described above. In such an example,in order for the storage system (402) to attach (508) to the pod, a poddefinition stored locally within each of the storage systems (402, 404,406) in the pod may need to be updated in order for the storage system(402) to attach (508) to the pod. In such an example, each storagesystem member of a pod may have its own copy of the membership,including which storage systems it last knew were in-sync, and whichstorage systems it last knew comprised the entire set of pod members.

In the example method depicted in FIG. 5 , the storage system (402) mayalso receive (510) a request to read a portion of the dataset (426) andthe storage system (402) may process (512) the request to read theportion of the dataset (426) locally. Readers will appreciate thatalthough requests to modify (e.g., a write operation) the dataset (426)require coordination between the storage systems (402, 404, 406) in apod, as the dataset (426) should be consistent across all storagesystems (402, 404, 406) in a pod, responding to a request to read aportion of the dataset (426) does not require similar coordinationbetween the storage systems (402, 404, 406). As such, a particularstorage system (402) that receives a read request may service the readrequest locally by reading a portion of the dataset (426) that is storedwithin the storage system's (402) storage devices, with no synchronouscommunication with other storage systems (404, 406) in the pod. Readrequests received by one storage system for a replicated dataset in areplicated cluster are expected to avoid any communication in the vastmajority of cases, at least when received by a storage system that isrunning within a cluster that is also running nominally. Such readsshould normally be processed simply by reading from the local copy of aclustered dataset with no further interaction required with otherstorage systems in the cluster

Readers will appreciate that the storage systems may take steps toensure read consistency such that a read request will return the sameresult regardless of which storage system processes the read request.For example, the resulting clustered dataset content for any set ofupdates received by any set of storage systems in the cluster should beconsistent across the cluster, at least at any time updates are idle(all previous modifying operations have been indicated as complete andno new update requests have been received and processed in any way).More specifically, the instances of a clustered dataset across a set ofstorage systems can differ only as a result of updates that have not yetcompleted. This means, for example, that any two write requests whichoverlap in their volume block range, or any combination of a writerequest and an overlapping snapshot, compare-and-write, or virtual blockrange copy, must yield a consistent result on all copies of the dataset.Two operations cannot yield a result as if they happened in one order onone storage system and a different order on another storage system inthe replicated cluster.

Furthermore, read requests may be time order consistent. For example, ifone read request is received on a replicated cluster and completed andthat read is then followed by another read request to an overlappingaddress range which is received by the replicated cluster and where oneor both reads in any way overlap in time and volume address range with amodification request received by the replicated cluster (whether any ofthe reads or the modification are received by the same storage system ora different storage system in the replicated cluster), then if the firstread reflects the result of the update then the second read should alsoreflect the results of that update, rather than possibly returning datathat preceded the update. If the first read does not reflect the update,then the second read can either reflect the update or not. This ensuresthat between two read requests “time” for a data segment cannot rollbackward.

In the example method depicted in FIG. 5 , the storage system (402) mayalso detect (514) a disruption in data communications with one or moreof the other storage systems (404, 406). A disruption in datacommunications with one or more of the other storage systems (404, 406)may occur for a variety of reasons. For example, a disruption in datacommunications with one or more of the other storage systems (404, 406)may occur because one of the storage systems (402, 404, 406) has failed,because a network interconnect has failed, or for some other reason. Animportant aspect of synchronous replicated clustering is ensuring thatany fault handling doesn't result in unrecoverable inconsistencies, orany inconsistency in responses. For example, if a network fails betweentwo storage systems, at most one of the storage systems can continueprocessing newly incoming I/O requests for a pod. And, if one storagesystem continues processing, the other storage system can't process anynew requests to completion, including read requests.

In the example method depicted in FIG. 5 , the storage system (402) mayalso determine (516) whether to the particular storage system (402)should remain online as part of the pod. As mentioned above, to be‘online’ as part of a pod, a storage system must consider itself to bein-sync for the pod and must be communicating with all other storagesystems it considers to be in-sync for the pod. If a storage systemcan't be certain that it is in-sync and communicating with all otherstorage systems that are in-sync, then it may stop processing newincoming requests to access the dataset (426). As such, the storagesystem (402) may determine (516) whether to the particular storagesystem (402) should remain online as part of the pod, for example, bydetermining whether it can communicate with all other storage systems(404, 406) it considers to be in-sync for the pod (e.g., via one or moretest messages), by determining whether the all other storage systems(404, 406) it considers to be in-sync for the pod also consider thestorage system (402) to be attached to the pod, through a combination ofboth steps where the particular storage system (402) must confirm thatit can communicate with all other storage systems (404, 406) itconsiders to be in-sync for the pod and that all other storage systems(404, 406) it considers to be in-sync for the pod also consider thestorage system (402) to be attached to the pod, or through some othermechanism.

In the example method depicted in FIG. 5 , the storage system (402) mayalso, responsive to affirmatively (518) determining that the particularstorage system (402) should remain online as part of the pod, keep (522)the dataset (426) on the particular storage system (402) accessible formanagement and dataset operations. The storage system (402) may keep(522) the dataset (426) on the particular storage system (402)accessible for management and dataset operations, for example, byaccepting requests to access the version of the dataset (426) that isstored on the storage system (402) and processing such requests, byaccepting and processing management operations associated with thedataset (426) that are issued by a host or authorized administrator, byaccepting and processing management operations associated with thedataset (426) that are issued by one of the other storage systems (404,406) in the pod, or in some other way.

In the example method depicted in FIG. 5 , the storage system (402) mayalso, responsive to determining that the particular storage systemshould not (520) remain online as part of the pod, make (524) thedataset (426) on the particular storage system (402) inaccessible formanagement and dataset operations. The storage system (402) may make(524) the dataset (426) on the particular storage system (402)inaccessible for management and dataset operations, for example, byrejecting requests to access the version of the dataset (426) that isstored on the storage system (402), by rejecting management operationsassociated with the dataset (426) that are issued by a host or otherauthorized administrator, by rejecting management operations associatedwith the dataset (426) that are issued by one of the other storagesystems (404, 406) in the pod, or in some other way.

In the example method depicted in FIG. 5 , the storage system (402) mayalso detect (526) that the disruption in data communications with one ormore of the other storage systems (404, 406) has been repaired. Thestorage system (402) may detect (526) that the disruption in datacommunications with one or more of the other storage systems (404, 406)has been repaired, for example, by receiving a message from the one ormore of the other storage systems (404, 406). In response to detecting(526) that the disruption in data communications with one or more of theother storage systems (404, 406) has been repaired, the storage system(402) may make (528) the dataset (426) on the particular storage system(402) accessible for management and dataset operations.

Readers will appreciate that the example depicted in FIG. 5 describes anembodiment in which various actions are depicted as occurring withinsome order, although no ordering is required. Furthermore, otherembodiments may exist where the storage system (402) only carries out asubset of the described actions. For example, the storage system (402)may perform the steps of detecting (514) a disruption in datacommunications with one or more of the other storage systems (404, 406),determining (516) whether to the particular storage system (402) shouldremain in the pod, keeping (522) the dataset (426) on the particularstorage system (402) accessible for management and dataset operations ormaking (524) the dataset (426) on the particular storage system (402)inaccessible for management and dataset operations without firstreceiving (510) a request to read a portion of the dataset (426) andprocessing (512) the request to read the portion of the dataset (426)locally. Furthermore, the storage system (402) may detect (526) that thedisruption in data communications with one or more of the other storagesystems (404, 406) has been repaired and make (528) the dataset (426) onthe particular storage system (402) accessible for management anddataset operations without first receiving (510) a request to read aportion of the dataset (426) and processing (512) the request to readthe portion of the dataset (426) locally. In fact, none of the stepsdescribed herein are explicitly required in all embodiments asprerequisites for performing other steps described herein.

For further explanation, FIG. 6 sets forth a flow chart illustratingsteps that may be performed by storage systems (402, 404, 406) thatsupport a pod according to some embodiments of the present disclosure.Although depicted in less detail, the storage systems (402. 404, 406)depicted in FIG. 6 may be similar to the storage systems described abovewith reference to FIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, FIG. 4 , or anycombination thereof. In fact, the storage systems (402, 404, 406)depicted in FIG. 6 may include the same, fewer, additional components asthe storage systems described above.

In the example method depicted in FIG. 6 , two or more of the storagesystems (402, 404) may each identify (608) a target storage system (618)for asynchronously receiving the dataset (426). The target storagesystem (618) for asynchronously receiving the dataset (426) may beembodied, for example, as a backup storage system that is located in adifferent data center than either of the storage systems (402, 404) thatare members of a particular pod, as cloud storage that is provided by acloud services provider, or in many other ways. Readers will appreciatethat the target storage system (618) is not one of the plurality ofstorage systems (402, 404) across which the dataset (426) issynchronously replicated, and as such, the target storage system (618)initially does not include an up-to-date local copy of the dataset(426).

In the example method depicted in FIG. 6 , two or more of the storagesystems (402, 404) may each also identify (610) a portion of the dataset(426) that is not being asynchronously replicated to the target storage(618) system by any of the other storages systems that are members of apod that includes the dataset (426). In such an example, the storagesystems (402, 404) may each asynchronously replicate (612), to thetarget storage system (618), the portion of the dataset (426) that isnot being asynchronously replicated to the target storage system by anyof the other storages systems. Consider an example in which a firststorage system (402) is responsible for asynchronously replicating afirst portion (e.g., a first half of an address space) of the dataset(426) to the target storage system (618). In such an example, the secondstorage system (404) would be responsible for asynchronously replicatinga second portion (e.g., a second half of an address space) of thedataset (426) to the target storage system (618), such that the two ormore storage systems (402, 404) collectively replicate the entiredataset (426) to the target storage system (618).

Readers will appreciate that through the use of pods, as describedabove, the replication relationship between two storage systems may beswitched from a relationship where data is asynchronously replicated toa relationship where data is synchronously replicated. For example, ifstorage system A is configured to asynchronously replicate a dataset tostorage system B, creating a pod that includes the dataset, storagesystem A as a member, and storage system B as a member can switch therelationship where data is asynchronously replicated to a relationshipwhere data is synchronously replicated. Likewise, through the use ofpods, the replication relationship between two storage systems may beswitched from a relationship where data is synchronously replicated to arelationship where data is asynchronously replicated. For example, if apod is created that includes the dataset, storage system A as a member,and storage system B as a member, by merely unstretching the pod (toremove storage system A as a member or to remove storage system B as amember), a relationship where data is synchronously replicated betweenthe storage systems can immediately be switched to a relationship wheredata is asynchronously replicated. In such a way, storage systems mayswitch back-and-forth as needed between asynchronous replication andsynchronous replication.

This switching can be facilitated by the implementation relying onsimilar techniques for both synchronous and asynchronous replication.For example, if resynchronization for a synchronously replicated datasetrelies on the same or a compatible mechanism as is used for asynchronousreplication, then switching to asynchronous replication is conceptuallyidentical to dropping the in-sync state and leaving a relationship in astate similar to a “perpetual recovery” mode. Likewise, switching fromasynchronous replication to synchronous replication can operateconceptually by “catching up” and becoming in-sync just as is done whencompleting a resynchronization with the switching system becoming anin-sync pod member.

Alternatively, or additionally, if both synchronous and asynchronousreplication rely on similar or identical common metadata, or a commonmodel for representing and identifying logical extents or stored blockidentities, or a common model for representing content-addressablestored blocks, then these aspects of commonality can be leveraged todramatically reduce the content that may need to be transferred whenswitching to and from synchronous and asynchronous replication. Further,if a dataset is asynchronously replicated from a storage system A to astorage system B, and system B further asynchronously replicates thatdata set to a storage system C, then a common metadata model, commonlogical extent or block identities, or common representation ofcontent-addressable stored blocks, can dramatically reduce the datatransfers needed to enable synchronous replication between system A andsystem C.

Readers will further appreciate that that through the use of pods, asdescribed above, replication techniques may be used to perform tasksother than replicating data. In fact, because a pod may include a set ofmanaged objects, tasks like migrating a virtual machine may be carriedout using pods and the replication techniques described herein. Forexample, if virtual machine A is executing on storage system A, bycreating a pod that includes virtual machine A as a managed object,storage system A as a member, and storage system B as a member, virtualmachine A and any associated images and definitions may be migrated tostorage system B, at which time the pod could simply be destroyed,membership could be updated, or other actions may be taken as necessary.

For further explanation, FIG. 7 sets forth diagrams of metadatarepresentations that may be implemented as a structured collection ofmetadata objects that, together, may represent a logical volume ofstorage data, or a portion of a logical volume, in accordance with someembodiments of the present disclosure. Metadata representations 750,754, and 760 may be stored within a storage system (706), and one ormore metadata representations may be generated and maintained for eachof multiple storage objects, such as volumes, or portions of volumes,stored within a storage system (706).

While other types of structured collections of the metadata objects arepossible, in this example, metadata representations may be structured asa directed acyclic graph (DAG) of nodes, where, to maintain efficientaccess to any given node, the DAG may be structured and balancedaccording to various methods. For example, a DAG for a metadatarepresentation may be defined as a type of B-tree, and balancedaccordingly in response to changes to the structure of the metadatarepresentation, where changes to the metadata representation may occurin response to changes to, or additions to, underlying data representedby the metadata representation. While in this example, there are onlytwo levels for the sake of simplicity, in other examples, metadatarepresentations may span across multiple levels and may include hundredsor thousands of nodes, where each may include any number of links toother nodes.

Further, in this example, the leaves of a metadata representation mayinclude pointers to the stored data for a volume, or portion of avolume, where a logical address, or a volume and offset, may be used toidentify and navigate through the metadata representation to reach oneor more leaf nodes that reference stored data corresponding to thelogical address. For example, a volume (752) may be represented by ametadata representation (750), which includes multiple metadata objectnodes (752, 752A-752N), where leaf nodes (752A-752N) include pointers torespective data objects (753A-753N, 757). Data objects may be any sizeunit of data within a storage system (706). For example, data objects(753A-753N, 757) may each be a logical extent, where logical extents maybe some specified size, such as 1 MB, 4 MB, or some other size.

In this example, a snapshot (756) may be created as a snapshot of astorage object, in this case, a volume (752), where at the point in timewhen the snapshot (756) is created, the metadata representation (754)for the snapshot (756) includes all of the metadata objects for themetadata representation (750) for the volume (752). Further, in responseto creation of the snapshot (756), the metadata representation (754) maybe designated to be read only. However, the volume (752) sharing themetadata representation may continue to be modified, and while at themoment the snapshot is created, the metadata representations for thevolume (752) and the snapshot (756) are identical, as modifications aremade to data corresponding to the volume (752), and in response to themodifications, the metadata representations for the volume (752) and thesnapshot (756) may diverge and become different.

For example, given a metadata representation (750) to represent a volume(752) and a metadata representation (754) to represent a snapshot (756),the storage system (706) may receive an I/O operation that writes todata that is ultimately stored within a particular data object (753B),where the data object (753B) is pointed to by a leaf node pointer(752B), and where the leaf node pointer (752B) is part of both metadatarepresentations (750, 754). In response to the write operation, the readonly data objects (753A-753N) referred to by the metadata representation(754) remain unchanged, and the pointer (752B) may also remainunchanged. However, the metadata representation (750), which representsthe current volume (752), is modified to include a new data object tohold the data written by the write operation, where the modifiedmetadata representation is depicted as the metadata representation(760). Further, the write operation may be directed to only a portion ofthe data object (753B), and consequently, the new data object (757) mayinclude a copy of previous contents of the data object (753B) inaddition to the payload for the write operation.

In this example, as part of processing the write operation, the metadatarepresentation (760) for the volume (752) is modified to remove anexisting metadata object pointer (752B) and to include a new metadataobject pointer (758), where the new metadata object pointer (758) isconfigured to point to a new data object (757), where the new dataobject (757) stores the data written by the write operation. Further,the metadata representation (760) for the volume (752) continues toinclude all metadata objects included within the previous metadatarepresentation (750)—with the exclusion of the metadata object pointer(752B) that referenced the target data object, where the metadata objectpointer (752B) continues to reference the read only data object (753B)that would have been overwritten.

In this way, using metadata representations, a volume or a portion of avolume may be considered to be snapshotted, or considered to be copied,by creating metadata objects, and without actual duplication of dataobjects—where the duplication of data objects may be deferred until awrite operation is directed at one of the read only data objectsreferred to by the metadata representations.

In other words, an advantage of using a metadata representation torepresent a volume is that a snapshot or a copy of a volume may becreated and be accessible in constant order time, and specifically, inthe time it takes to create a metadata object for the snapshot or copy,and to create a reference for the snapshot or copy metadata object tothe existing metadata representation for the volume being snapshotted orcopied.

As an example use, a virtualized copy-by-reference may make use of ametadata representation in a manner that is similar to the use of ametadata representation in creating a snapshot of a volume—where ametadata representation for a virtualized copy-by-reference may oftencorrespond to a portion of a metadata representation for an entirevolume. An example implementation of virtualized copy-by-reference maybe within the context of a virtualized storage system, where multipleblock ranges within and between volumes may reference a unified copy ofstored data. In such virtualized storage system, the metadata describedabove may be used to handle the relationship between virtual, orlogical, addresses and physical, or real, addresses—in other words, themetadata representation of stored data enables a virtualized storagesystem that may be considered flash-friendly in that it reduces, orminimizes, wear on flash memory.

In some examples, logical extents may be combined in various ways,including as simple collections or as logically related address rangeswithin some larger-scale logical extent that is formed as a set oflogical extent references. These larger combinations could also be givenlogical extent identities of various kinds, and could be furthercombined into still larger logical extents or collections. Acopy-on-write status could apply to various layers, and in various waysdepending on the implementation. For example, a copy on write statusapplied to a logical collection of logical collections of extents mightresult in a copied collection retaining references to unchanged logicalextents and the creation of copied-on-write logical extents (throughcopying references to any unchanged stored data blocks as needed) whenonly part of the copy-on-write logical collection is changed.

Deduplication, volume snapshots, or block range snapshots may beimplemented in this model through combinations of referencing storeddata blocks, or referencing logical extents, or marking logical extents(or identified collections of logical extents) as copy-on-write.

Further, with flash storage systems, stored data blocks may be organizedand grouped together in various ways as collections are written out intopages that are part of larger erase blocks. Eventual garbage collectionof deleted or replaced stored data blocks may involve moving contentstored in some number of pages elsewhere so that an entire erase blockcan be erased and prepared for reuse. This process of selecting physicalflash pages, eventually migrating and garbage collecting them, and thenerasing flash erase blocks for reuse may or may not be coordinated,driven by, or performed by the aspect of a storage system that is alsohandling logical extents, deduplication, compression, snapshots, virtualcopying, or other storage system functions. A coordinated or drivenprocess for selecting pages, migrating pages, garbage collecting anderasing erase blocks may further take into account variouscharacteristics of the flash memory device cells, pages, and eraseblocks such as number of uses, aging predictions, adjustments to voltagelevels or numbers of retries needed in the past to recover stored data.They may also take into account analysis and predictions across allflash memory devices within the storage system.

To continue with this example, where a storage system may be implementedbased on directed acyclic graphs comprising logical extents, logicalextents can be categorized into two types: leaf logical extents, whichreference some amount of stored data in some way, and composite logicalextents, which reference other leaf or composite logical extents.

A leaf extent can reference data in a variety of ways. It can pointdirectly to a single range of stored data (e.g., 64 kilobytes of data),or it can be a collection of references to stored data (e.g., a 1megabyte “range” of content that maps some number of virtual blocksassociated with the range to physically stored blocks). In the lattercase, these blocks may be referenced using some identity, and someblocks within the range of the extent may not be mapped to anything.Also, in that latter case, these block references need not be unique,allowing multiple mappings from virtual blocks within some number oflogical extents within and across some number of volumes to map to thesame physically stored blocks. Instead of stored block references, alogical extent could encode simple patterns: for example, a block whichis a string of identical bytes could simply encode that the block is arepeated pattern of identical bytes.

A composite logical extent can be a logical range of content with somevirtual size, which comprises a plurality of maps that each map from asubrange of the composite logical extent logical range of content to anunderlying leaf or composite logical extent. Transforming a requestrelated to content for a composite logical extent, then, involves takingthe content range for the request within the context of the compositelogical extent, determining which underlying leaf or composite logicalextents that request maps to, and transforming the request to apply toan appropriate range of content within those underlying leaf orcomposite logical extents.

Volumes, or files or other types of storage objects, can be described ascomposite logical extents. Thus, these presented storage objects can beorganized using this extent model.

Depending on implementation, leaf or composite logical extents could bereferenced from a plurality of other composite logical extents,effectively allowing inexpensive duplication of larger collections ofcontent within and across volumes. Thus, logical extents can be arrangedessentially within an acyclic graph of references, each ending in leaflogical extents. This can be used to make copies of volumes, to makesnapshots of volumes, or as part of supporting virtual range copieswithin and between volumes as part of EXTENDED COPY or similar types ofoperations.

An implementation may provide each logical extent with an identity whichcan be used to name it. This simplifies referencing, since thereferences within composite logical extents become lists comprisinglogical extent identities and a logical subrange corresponding to eachsuch logical extent identity. Within logical extents, each stored datablock reference may also be based on some identity used to name it.

To support these duplicated uses of extents, we can add a furthercapability: copy-on-write logical extents. When a modifying operationaffects a copy-on-write leaf or composite logical extent the logicalextent is copied, with the copy being a new reference and possiblyhaving a new identity (depending on implementation). The copy retainsall references or identities related to underlying leaf or compositelogical extents, but with whatever modifications result from themodifying operation. For example, a WRITE, WRITE SAME, XDWRITEREAD,XPWRITE, or COMPARE AND WRITE request may store new blocks in thestorage system (or use deduplication techniques to identify existingstored blocks), resulting in modifying the corresponding leaf logicalextents to reference or store identities to a new set of blocks,possibly replacing references and stored identities for a previous setof blocks. Alternately, an UNMAP request may modify a leaf logicalextent to remove one or more block references. In both types of cases, aleaf logical extent is modified. If the leaf logical extent iscopy-on-write, then a new leaf logical extent will be created that isformed by copying unaffected block references from the old extent andthen replacing or removing block references based on the modifyingoperation.

A composite logical extent that was used to locate the leaf logicalextent may then be modified to store the new leaf logical extentreference or identity associated with the copied and modified leaflogical extent as a replacement for the previous leaf logical extent. Ifthat composite logical extent is copy-on-write, then a new compositelogical extent is created as a new reference or with a new identity, andany unaffected references or identities to its underlying logicalextents are copied to that new composite logical extent, with theprevious leaf logical extent reference or identity being replaced withthe new leaf logical extent reference or identity.

This process continues further backward from referenced extent toreferencing composite extent, based on the search path through theacyclic graph used to process the modifying operation, with allcopy-on-write logical extents being copied, modified, and replaced.

These copied leaf and composite logical extents can then drop thecharacteristic of being copy on write, so that further modifications donot result in an additional copy. For example, the first time someunderlying logical extent within a copy-on-write “parent” compositeextent is modified, that underlying logical extent may be copied andmodified, with the copy having a new identity which is then written intoa copied and replaced instance of the parent composite logical extent.However, a second time some other underlying logical extent is copiedand modified and with that other underlying logical extent copy's newidentity being written to the parent composite logical extent, theparent can then be modified in place with no further copy and replacenecessary on behalf of references to the parent composite logicalextent.

Modifying operations to new regions of a volume or of a compositelogical extent for which there is no current leaf logical extent maycreate a new leaf logical extent to store the results of thosemodifications. If that new logical extent is to be referenced from anexisting copy-on-write composite logical extent, then that existingcopy-on-write composite logical extent will be modified to reference thenew logical extent, resulting in another copy, modify, and replacesequence of operations similar to the sequence for modifying an existingleaf logical extent.

If a parent composite logical extent cannot be grown large enough (basedon implementation) to cover an address range associated that includesnew leaf logical extents to create for a new modifying operation, thenthe parent composite logical extent may be copied into two or more newcomposite logical extents which are then referenced from a single“grandparent” composite logical extent which yet again is a newreference or a new identity. If that grandparent logical extent isitself found through another composite logical extent that iscopy-on-write, then that another composite logical extent will be copiedand modified and replaced in a similar way as described in previousparagraphs. This copy-on-write model can be used as part of implementingsnapshots, volume copies, and virtual volume address range copies withina storage system implementation based on these directed acyclic graphsof logical extents. To make a snapshot as a read-only copy of anotherwise writable volume, a graph of logical extents associated withthe volume is marked copy-on-write and a reference to the originalcomposite logical extents are retained by the snapshot. Modifyingoperations to the volume will then make logical extent copies as needed,resulting in the volume storing the results of those modifyingoperations and the snapshots retaining the original content. Volumecopies are similar, except that both the original volume and the copiedvolume can modify content resulting in their own copied logical extentgraphs and subgraphs.

Virtual volume address range copies can operate either by copying blockreferences within and between leaf logical extents (which does notitself involve using copy-on-write techniques unless changes to blockreferences modifies copy-on-write leaf logical extents). Alternately,virtual volume address range copies can duplicate references to leaf orcomposite logical extents, which works well for volume address rangecopies of larger address ranges. Further, this allows graphs to becomedirected acyclic graphs of references rather than merely referencetrees. Copy-on-write techniques associated with duplicated logicalextent references can be used to ensure that modifying operations to thesource or target of a virtual address range copy will result in thecreation of new logical extents to store those modifications withoutaffecting the target or the source that share the same logical extentimmediately after the volume address range copy operation.

Input/output operations for pods may also be implemented based onreplicating directed acyclic graphs of logical extents. For example,each storage system within a pod could implement private graphs oflogical extents, such that the graphs on one storage system for a podhave no particular relationship to the graphs on any second storagesystem for the pod. However, there is value in synchronizing the graphsbetween storage systems in a pod. This can be useful forresynchronization and for coordinating features such as asynchronous orsnapshot based replication to remote storage systems. Further, it may beuseful for reducing some overhead for handling the distribution ofsnapshot and copy related processing. In such a model, keeping thecontent of a pod in sync across all in-sync storage systems for a pod isessentially the same as keeping graphs of leaf and composite logicalextents in sync for all volumes across all in-sync storage systems forthe pod, and ensuring that the content of all logical extents isin-sync. To be in sync, matching leaf and composite logical extentsshould either have the same identity or should have mappable identities.Mapping could involve some set of intermediate mapping tables or couldinvolve some other type of identity translation. In some cases,identities of blocks mapped by leaf logical extents could also be keptin sync.

In a pod implementation based on a leader and followers, with a singleleader for each pod, the leader can be in charge of determining anychanges to the logical extent graphs. If a new leaf or composite logicalextent is to be created, it can be given an identity. If an existingleaf or composite logical extent is to be copied to form a new logicalextent with modifications, the new logical extent can be described as acopy of a previous logical extent with some set of modifications. If anexisting logical extent is to be split, the split can be described alongwith the new resulting identities. If a logical extent is to bereferenced as an underlying logical extent from some additionalcomposite logical extent, that reference can be described as a change tothe composite logical extent to reference that underlying logicalextent.

Modifying operations in a pod thus comprises distributing descriptionsof modifications to logical extent graphs (where new logical extents arecreated to extend content or where logical extents are copied, modified,and replaced to handle copy-on-write states related to snapshots, volumecopies, and volume address range copies) and distributing descriptionsand content for modifications to the content of leaf logical extents. Anadditional benefit that comes from using metadata in the form ofdirected acyclic graphs, as described above, is that I/O operations thatmodify stored data in physical storage may be given effect at a userlevel through the modification of metadata corresponding to the storeddata in physical storage—without modifying the stored data in physicalstorage. In the disclosed embodiments of storage systems, where thephysical storage may be a solid state drive, the wear that accompaniesmodifications to flash memory may be avoided or reduced due to I/Ooperations being given effect through the modifications of the metadatarepresenting the data targeted by the I/O operations instead of throughthe reading, erasing, or writing of flash memory. Further, as notedabove, in such a virtualized storage system, the metadata describedabove may be used to handle the relationship between virtual, orlogical, addresses and physical, or real, addresses—in other words, themetadata representation of stored data enables a virtualized storagesystem that may be considered flash-friendly in that it reduces, orminimizes, wear on flash memory.

Leader storage systems may perform their own local operations toimplement these descriptions in the context of their local copy of thepod dataset and the local storage system's metadata. Further, thein-sync followers perform their own separate local operations toimplement these descriptions in the context of their separate local copyof the pod dataset and their separate local storage system's metadata.When both leader and follower operations are complete, the result iscompatible graphs of logical extents with compatible leaf logical extentcontent. These graphs of logical extents then become a type of “commonmetadata” as described in previous examples. This common metadata can bedescribed as dependencies between modifying operations and requiredcommon metadata. Transformations to graphs can be described as separateoperations within a set of or more predicates that may describerelationships, such as dependencies, with one or more other operations.In other words, interdependencies between operations may be described asa set of precursors that one operation depends on in some way, where theset of precursors may be considered predicates that must be true for anoperation to complete. A fuller description of predicates may be foundwithin Application Reference Ser. No. 15/696,418, which is includedherein by reference in its entirety. Alternately, each modifyingoperation that relies on a particular same graph transformation that hasnot yet been known to complete across the pod can include the parts ofany graph transformation that it relies on. Processing an operationdescription that identifies a “new” leaf or composite logical extentthat already exists can avoid creating the new logical extent since thatpart was already handled in the processing of some earlier operation,and can instead implement only the parts of the operation processingthat change the content of leaf or composite logical extents. It is arole of the leader to ensure that transformations are compatible witheach other. For example, we can start with two writes come that come infor a pod. A first write replaces a composite logical extent A with acopy of formed as composite logical extent B, replaces a leaf logicalextent C with a copy as leaf logical extent D and with modifications tostore the content for the second write, and further writes leaf logicalextent D into composite logical extent B. Meanwhile, a second writeimplies the same copy and replacement of composite logical extent A withcomposite logical extent B but copies and replaces a different leaflogical extent E with a logical extent F which is modified to store thecontent of the second write, and further writes logical extent F intological extent B. In that case, the description for the first write caninclude the replacement of A with B and C with D and the writing of Dinto composite logical extent B and the writing of the content of thefirst write into leaf extend B; and, the description of the second writecan include the replacement of A with B and E with F and the writing ofF into composite logical extent B, along with the content of the secondwrite which will be written to leaf extent F. A leader or any followercan then separately process the first write or the second write in anyorder, and the end result is B copying and replacing A, D copying andreplacing C, F copying replacing E, and D and F being written intocomposite logical extent B. A second copy of A to form B can be avoidedby recognizing that B already exists. In this way, a leader can ensurethat the pod maintains compatible common metadata for a logical extentgraph across in-sync storage systems for a pod.

Given an implementation of storage systems using directed acyclic graphsof logical extents, recovery of pods based on replicated directedacyclic graphs of logical extents may be implemented. Specifically, inthis example, recovery in pods may be based on replicated extent graphsthen involves recovering consistency of these graphs as well asrecovering content of leaf logical extents. In this implementation ofrecovery, operations may include querying for graph transformations thatare not known to have completed on all in-sync storage systems for apod, as well as all leaf logical extent content modifications that arenot known to have completed across all storage systems for the pod. Suchquerying could be based on operations since some coordinated checkpoint,or could simply be operations not known to have completed where eachstorage system keeps a list of operations during normal operation thathave not yet been signaled as completed. In this example, graphtransformations are straightforward: a graph transformation may createnew things, copy old things to new things, and copy old things into twoor more split new things, or they modify composite extents to modifytheir references to other extents. Any stored operation descriptionfound on any in-sync storage system that creates or replaces any logicalextent can be copied and performed on any other storage system that doesnot yet have that logical extent. Operations that describe modificationsto leaf or composite logical extents can apply those modifications toany in-sync storage system that had not yet applied them, as long as theinvolved leaf or composite logical extents have been recovered properly.

In another example, as an alternative to using a logical extent graph,storage may be implemented based on a replicated content-addressablestore. In a content-addressable store, for each block of data (forexample, every 512 bytes, 4096 bytes, 8192 bytes or even 16384 bytes) aunique hash value (sometimes also called a fingerprint) is calculated,based on the block content, so that a volume or an extent range of avolume can be described as a list of references to blocks that have aparticular hash value. In a synchronously replicated storage systemimplementation based on references to blocks with the same hash value,replication could involve a first storage system receiving blocks,calculating fingerprints for those blocks, identifying block referencesfor those fingerprints, and delivering changes to one or a plurality ofadditional storage systems as updates to the mapping of volume blocks toreferenced blocks. If a block is found to have already been stored bythe first storage system, that storage system can use its reference toname the reference in each of the additional storage systems (eitherbecause the reference uses the same hash value or because an identifierfor the reference is either identical or can be mapped readily).Alternately, if a block is not found by the first storage system, thencontent of the first storage system may be delivered to other storagesystems as part of the operation description along with the hash valueor identity associated with that block content. Further, each in-syncstorage system's volume descriptions are then updated with the new blockreferences. Recovery in such a store may then include comparing recentlyupdated block references for a volume. If block references differbetween different in-sync storage systems for a pod, then one version ofeach reference can be copied to other storage systems to make themconsistent. If the block reference on one system does not exist, then itbe copied from some storage system that does store a block for thatreference. Virtual copy operations can be supported in such a block orhash reference store by copying the references as part of implementingthe virtual copy operation.

As described above, metadata may be synchronized among storage systemsthat are synchronously replicating a dataset. Such metadata may bereferred to as common metadata, or shared metadata, that is stored by astorage system on behalf of a pod related to the mapping of segments ofcontent stored within the pod to virtual address within storage objectswithin the pod, where information related to those mappings issynchronized between member storage systems for the pod to ensurecorrect behavior—or better performance—for storage operations related tothe pod. In some examples, a storage object may implement a volume or asnapshot. The synchronized metadata may include: (a) information to keepvolume content mappings synchronized among the storage systems in thepod; (b) tracking data for recovery checkpoints or for in-progress writeoperations; (c) information related to the delivery of data and mappinginformation to a remote storage system for asynchronous or periodicreplication.

Information to keep volume content mappings synchronized among thestorage systems in the pod may enable efficient creating of snapshots,which in turn enables that subsequent updates, copies of snapshots, orsnapshot removals may be performed efficiently and consistently acrossthe pod member storage systems.

Tracking data for recovery checkpoints or for in-progress writeoperations may enable efficient crash recovery and efficient detectionof content or volume mappings that may have been partially or completelyapplied on individual storage systems for a pod, but that may not havebeen completely applied on other storage systems for the pod.

Information related to the delivery of data and mapping information to aremote storage system for asynchronous or periodic replication mayenable more than one member storage system for a pod to serve as asource for the replicated pod content with minimal concerns for dealingwith mismatches in mapping and differencing metadata used to driveasynchronous or periodic replication.

In some examples, shared metadata may include descriptions for, orindications of, a named grouping, or identifiers for, of one or morevolumes or one or more storage objects that are a subset of an entiresynchronously replicated dataset for a pod—where such a of volumes orstorage objects of a dataset may be referred to as a consistency group.A consistency group may be defined to specify a subset of volumes orstorage objects of the dataset to be used for consistent snapshots,asynchronous replication, or periodic replication. In some examples, aconsistency group may be calculated dynamically, such as by includingall volumes connected to a particular set of hosts or host networkports, or that are connected to a particular set of applications orvirtual machines or containers, where the applications, virtualmachines, or containers may operate on external server systems or mayoperate on one or more of the storage systems that are members of a pod.In other examples, a consistency group may be defined according to userselections of a type of data or set of data, or specifications of aconsistency group similar to the dynamic calculation, where a user mayspecify, for example through a command or management console, that aparticular, or named, consistency group be created to include allvolumes connected to a particular set of hosts or host network ports, orbe created to include data for a particular set of applications orvirtual machines or containers.

In an example using a consistency group, a first consistency groupsnapshot of a consistency group may include a first set of snapshot forall volumes or other storage objects that are members of the consistencygroup at the time of the first dataset snapshot, with a secondconsistency group snapshot of the same consistency group including asecond set of snapshots for the volumes or other storage objects thatare members of the consistency group at the time of the second datasetsnapshot. In other examples, a snapshot of the dataset may be stored onone or more target storage systems in an asynchronous manner. Similarly,asynchronous replication of a consistency group may account for dynamicchanges to member volumes and other storage objects of the consistencygroup, where consistency group snapshots of the consistency group ateither the source or the target of the asynchronous replication linkinclude the volumes and other storage objects that are members inrelationship to the consistency group at the time that the datasetsnapshot relates to. In the case of a target of an asynchronousreplication connection, the time that the dataset snapshot relates todepends on the dynamic dataset of the sender as it was received and wasin process at the time of the consistency group snapshot on the target.For example, if a target of an asynchronous replication is, say, 2000operations behind, where some of those operations are consistency groupmember changes, where a first set of such changes are more than 2000operations ago for the source, and a second set of changes are withinthe last 2000, then a consistency group snapshot at that time on thetarget will account for the first set of member changes and will notaccount for the second set of changes. Other uses of the target ofasynchronous replication may similarly account for the nature of thetime of the dataset for the consistency group in determining the volumesor other storage objects (and their content) for those uses. Forexample, in the same case of asynchronous replication being 2000operations behind, use of the target for a disaster recovery failovermight start from a dataset that includes the volumes and other storageobjects (and their content) as they were 2000 operations ago at thesource. In this discussion, concurrent operations at the source (e.g.,writes, storage object creations or deletions, changes to propertiesthat affect inclusion or exclusion of volumes or other storage objectsor other data from a consistency group, or other operations that were inprogress and not signaled as completed at a same point in time) mightnot have a single well-defined ordering, so the count of operations onlyneeds to represent some plausible ordering based on any allowed orderingof concurrent operations on the source.

As another example using consistency groups, in the case of periodicreplication based on replication of consistency group snapshots, eachreplicated consistency group snapshot would include the volumes andother storage objects at the time each consistency group snapshot wasformed on the source. Ensuring that membership in a consistency group iskept consistent by using common, or shared, metadata, ensures that afault—or other change which may cause the source of replication, or thesystem that forms a dataset snapshot, to switch from one storage systemin a pod to another—does not lose information needed for properlyhandling those consistency group snapshots or the consistency groupreplication. Further, this type of handling may allow for multiplestorage systems that are members of a pod to concurrently serve assource systems for asynchronous or periodic replication.

Further, synchronized metadata describing mapping of segments to storageobjects is not limited to mappings themselves, and may includeadditional information such as sequence numbers (or some other value foridentifying stored data), timestamps, volume/snapshot relationships,checkpoint identities, trees or graphs defining hierarchies, or directedgraphs of mapping relationships, among other storage system information.

For further explanation, FIG. 8 sets forth a flow chart illustrating anexample method for mediating between storage systems synchronouslyreplicating a dataset according to some embodiments of the presentdisclosure. Although the example method depicted in FIG. 8 illustratesan embodiment in which a dataset (426) is synchronously replicatedacross only two storage systems (814, 824), the example depicted in FIG.8 can be extended to embodiments in which the dataset (426) issynchronously replicated across additional storage systems.

In the following examples, mediation among a set of storage systems(814, 824) for a pod allows the storage systems to resolve lostcommunication with a paired system, where communication may be lost dueto communication faults or some other kind of system fault. As describedbelow, solutions to mediation may include use of quorums and an externalcontrol system that dictates which of the storage systems shouldcontinue processing I/O operations directed to a pod dataset, and racingfor a resource such as a mediator. However, an advantage of mediation isthat it is simpler than quorum protocols, and mediation works well witha two storage system configuration for synchronously replicated storagesystems, which is a common configuration. Further, mediation may be morerobust and easier to configure than external control systems and manyother types of resources that may be raced against.

As depicted in FIG. 8 , multiple storage systems (814, 824) that aresynchronously replicating a dataset (426) may be in communication with amediation service (800) over a network (854)—where a mediation service(800) may resolve which storage system continues to service the datasetin the event of a communication fault between storage systems, in theevent of a storage system going offline, or due to some other triggeringevent. Mediation is advantageous because if the storage systems areunable to communicate with each other, they may be unable to maintain asynchronously replicated dataset, and any received requests to modify adataset would be unserviceable because otherwise the dataset wouldbecome unsynchronized. In this example, mediation services for storagesystems that are synchronously replicating a dataset may be provided bya mediation service (800) that is external to the storage systems (814,824). While in this example, there are only two storage systems (814,824) depicted, in general, some other number of two or more storagesystems may be part of an in-sync list that is synchronously replicatinga dataset. Specifically, if a first storage system (814) has detected atriggering event, such as loss of a communication link (816) to a secondstorage system (824), the first storage system (814) may contact anexternal mediation service (800) to determine whether it can safely takeover the task of removing the non-communicating storage system from anin-sync list that specifies the storage systems that are synchronizedwith respect to replicating a dataset. In other cases, the first storagesystem (814) may contact the external mediation service (800) anddetermine that it, the first storage system (814), may have been removedfrom the in-sync list by a second storage system. In these examples, thestorage systems (814, 824) need not be in continuous communication withthe external mediation service (800) because under normal conditions thestorage systems (814, 824) do not need any information from themediation service (800) to operate normally and to maintain synchronousreplication of a dataset (426). In other words, in this example, themediation service (800) may not have an active role in membershipmanagement of an in-sync list, and further, the mediation service (800)may not even be aware of the normal operation of the storage systems(814, 824) in the in-sync list. Instead, the mediation service (800) maysimply provide persistent information that is used by the storagesystems (814, 824) to determine membership in an in-sync list, or todetermine whether a storage system can act to detach another storagesystem.

In some examples, a mediation service (800) may be contacted by one ormore storage systems (814, 824) in response to a triggering event suchas a communication link failure preventing the storage systems (814,824) from communication with each other; however, each storage system(814, 824) may be able to communicate with the mediation service (800)over a communication channel that is different from the communicationchannel used between the storage systems (814, 824). Consequently, whilethe storage systems (814, 824) may be unable to communicate with eachother, yet each of the storage systems (814, 824) may still be incommunication with the mediation service (800), where the storagesystems (814, 824) may use the mediation service (800) to resolve whichstorage system may proceed to service data storage requests. Further,the storage system that wins mediation from the mediation service (800)may detach another storage system and update an in-sync list indicatingthe storage systems that may continue to synchronously replicate adataset (426). In some examples, a mediation service (800) may handlevarious types of requests, such as a request to set a membership listthat includes a requestor storage system and excludes another storagesystem. In this example, a request completes successfully if themediation service (800) currently lists the requestor as a member, andthe request fails if the mediation service (800) does not currently listthe requestor as a member. In this way, if two storage systems (814,824) are each making requests at approximately the same time, where therequests serve to exclude the other, then the first request received maysucceed—where the mediation service sets the membership list to excludethe other storage system according to the first request—and the secondrequest received may fail because the membership list has been set toexclude it. The mutually exclusive access to a shared resource storing amembership list serves to ensure that only a single system as a time isallowed to set a membership list.

In another example, mediation may be based on a partition identifier,where a value may be defined to indicate a pod membership partitionidentifier to assert that membership has partitioned off, or removed,some set of storage systems from a pod. A ‘pod’, as the term is usedhere and throughout the remainder of the present application, may beembodied as a management entity that represents a dataset, a set ofmanaged objects and management operations, a set of access operations tomodify or read the dataset, and a plurality of storage systems. Suchmanagement operations may modify or query managed objects equivalentlythrough any of the storage systems, where access operations to read ormodify the dataset operate equivalently through any of the storagesystems. Each storage system may store a separate copy of the dataset asa proper subset of the datasets stored and advertised for use by thestorage system, where operations to modify managed objects or thedataset performed and completed through any one storage system arereflected in subsequent management objects to query the pod orsubsequent access operations to read the dataset. Additional detailsregarding a ‘pod’ may be found in previously filed provisional patentapplication No. 62/518,071, which is incorporated herein by reference.

A partition identifier may be local information stored on a givenstorage system, in addition to the given storage system storing a podmembership list. Systems that are in proper communication with eachother and are in-sync may have the same partition identifier, and whenstorage systems are added to a pod, then the current partitionidentifier may be copied along with the pod data contents. In thisexample, when one set of storage systems is not communicating withanother set of storage systems, one storage system from each set maycome up with a new and unique partition identifier and attempt to set itin the shared resource maintained by the mediation service (800) byusing a particular operation that succeeds for a storage system thatfirst acquires a lock on the shared resource, where another storagesystem—that failed to acquire a lock on the shared resource—fails anattempt at performing the particular operation. In one implementation,an atomic compare-and-set operation may be used, where the lastpartition identifier value stored by the mediation service (800) may beprovided by a storage system to have the permission to change thepartition identifier to a new value. In this example, a compare-and-setoperation may be successful for a storage system that is aware of thecurrent partition identifier value—where a storage system that firstsets the partition identifier value would be the storage system aware ofthe current partition identifier value. Further, a conditional-store ora PUT operation, which may be available in web service protocols, maywork to set the partition identifier value as described in this example.In other cases, such as in a SCSI environment, a compare-and-writeoperation may be used. In still other cases, the mediation service (800)may perform the compare-and-set operation by receiving a request from astorage system, where the request indicates an old partition identifiervalue and also a new partition identifier value, and where the mediationservice (800) changes the stored partition identifier to the newpartition identifier value if and only if the currently stored value isequal to the old partition identifier.

In this way, mediation based on a partition identifier may be used topersist information that may be used by storage systems to determinewhether or not a given storage system is included within a partitionedoff set of consistent pod members. In some cases, a partition identifiermay only change in the case of a spontaneous detach due to a fault ineither a storage system or a network interconnect. In these examples, astorage system that brings itself offline for a pod in a controlled waymay communicate with other storage systems to remove itself as anin-sync pod member, thus not requiring the formation of a mediated newpartition identifier. Further, a storage system that removes itself as amember of an in-sync pod may then add itself back as an in-sync podmember in a controlled way that does not require a mediated newpartition identifier. In addition, new storage system may be added tothe in-sync pod as long as the storage systems are communicating within-sync pod members, where the new storage systems may add themselves ina controlled way that does not require a mediated new partitionidentifier.

Consequently, an advantage of the mediated partition identifiermechanism is that the mediation service (800) may only be necessary whenthere is a fault, or other triggering event, that at least one set ofstorage systems react to by attempting to remove one or morenon-communicating storage systems from the in-sync pod membership list,where the non-communicating storage systems may attempt to do the same,but in reverse. Another advantage is that a mediation service (800) maybe less than absolutely reliable and have little impact on theavailability of the overall storage service provided by in-sync podmembers. For example, if two synchronously replicated storage systemseach fail once per year, then unless the mediation service (800) isunavailable at the exact moment a first of the two storage systems fail,the second storage system should successfully mediate to remove thefirst storage system. In short, if the mediation service (800) is up andavailable at least 99% of the time, the probability of the mediationservice (800) not being available when needed becomes exceedingly low.In this example, the chances would be only 1 out of 100 (1% or less)that the mediation service (800) would not be available at a criticaltime—which can reduce a once-a-year outage into a once-a-century outage.However, to lessen the odds of unavailability of a mediation service(800), the mediation service (800) may be monitored on a periodic basisto alert an administrator if a mediation service is not generallyavailable, where the mediation service (800) may also monitor storagesystems to generate an alert in case a particular storage system becomesunavailable.

In another example, as an alternative to using a partition identifierassociated with in-sync members for a pod, the mediation service (800)may provide a one-time mediation race target. Specifically, each timethe in-sync member storage systems for a pod may need to allow for thepossibility that one storage system may be detached by others, amediation race target may be established. For example, an agreed-uponkey in a table of mediation values may be set one time to a new value,where to win mediation, a storage system sets the agreed-upon key to aunique value that no other separately racing storage system would use.Previous to the mediation race, the agreed-upon key may not exist, or ifit does exist, it may be set to some agreed-upon precursor value such asan UNSET, or null, value. In this example, an operation to set the keyto a particular value succeeds if the key does not exist, if the key isin the UNSET state, or if the key is being set to a value equal to acurrent value—otherwise, the operation to set the key fails. Once a setof storage systems wins mediation, the remaining set of storage systemsmay define a new key to use for future mediations. In this example, astorage system may record the value it uses prior to the mediation raceso that the storage system may use the value again if it faults andrecovers, or reboots, before learning that it may have won the mediationrace. If two or more storage systems are communicating and are togetherracing against some other set of storage systems that are notcommunicating, this value may be shared to those other communicatingstorage system so that any one of them may continue the mediation race,and perhaps engage in a second mediation race, after some additionalsequence of faults. For example, it may be necessary for correctness torace for or validate the first mediation race target before racing for aunique value for a second mediation race target. In particular, thissequence may be necessary until a second mediation race target isreliably distributed to all storage systems that share the firstmediation race target and all storage systems are made aware that it hasbeen reliably distributed. At that point, there may be no continuingneed to first race for the first mediation target before racing for thesecond mediation target.

In some examples, a mediation service (800) may be managed on computersystems provided by an organization other than an organization or ownerof the storage systems being mediated. For example, if a vendor sellstwo storage systems to a customer, the vendor may host the mediators onservers provided in vendor-owned or managed data centers, or the vendormay contract with a cloud services provider to host the service. Avendor may also ensure that the mediation service is sufficientlyreliable and distinct from any of the customer's fault zones. In onecase, without excluding other cloud services providers, the mediationservice may be hosted in Amazon Web Services™, and the mediation servicemay be implemented with DynamoDB for reliable database service, whereDynamoDB may provide support for conditional-store primitives as web APIdatabase updates. In some cases, a mediation service may be implementedto operate across multiple cloud services provider regions or faultzones to further improve reliability. One advantage of using a vendor toprovide mediation services is that the mediation service isstraightforward to configure. Further, during creation of a pod astorage system may obtain a cryptographic token from the mediationservice, and store the cryptographic token in addition to storing apartition identifier and a pod membership list—where the cryptographictoken may be used to securely communicate the unique mediation serviceinformation for a pod.

In some cases, the mediation service (800) may be unavailable when astorage system attempts to mediate, and the following method provides aprocess of recovering, at least eventually, from such a service outage.For example, if a first set of storage systems attempts to detach asecond set of storage systems through a mediation service, but the firstset of storage systems cannot communicate with the mediation service(800), then the first set of storage systems cannot complete the detachoperation and cannot continue serving the pod. In some cases, if the twosets of storage systems manage to reconnect with each other, such thatall in-sync storage systems are communicating again—but with themediation service (800) still being unavailable—the two sets of storagesystems may synchronize and resume servicing the pod. However, in thisexample, one or more requests may have been sent to the mediationservice (800) to change the partition identifier, or to change whateverother properties associated with mediation, and none of the storagesystems may be certain whether a request was or was not received andprocessed, where a confirming response may have been lost. As a result,if there is a set of faulted storage systems or network interconnects,then no storage system may be sure which value to assert for thepartition identifier if and when the mediation service (800) comes backonline. In such a scenario, it is preferable for the pod's service toresume either when all in-sync storage systems come back online andresume communicating, or when an in-sync storage system can reconnect tothe mediation service (800). In one implementation, when all in-syncstorage systems reconnect, the in-sync storage systems all exchangeknown partition identifier values that may have been sent to themediation service (800). For example, if two storage systems had eachtried to change the partition identifier value, where one storage systemattempts to change the partition identifier to, say, 1749137481890, andanother storage system attempts to change the partition identifier to,say, 87927401839, and the last value known to have been acknowledged bythe mediation service (800) was 79223402936, then the mediation service(800) may currently store any of these three partition identifiervalues. As a result, any future attempt to change the mediationpartition identifier to a new value may supply any or all of these threepartition identifiers in attempts to gain the authority to make thechange. Further, a fourth attempt to change the partition identifiervalue may also encounter a fault, resulting in a fourth value that mayneed to be remembered by any storage systems that later attempts yetanother mediation. In addition, if any storage system successfullychanges the mediation service (800) partition identifier value, thatstorage system may purge the older partition identifier values from anyin-sync storage systems and from any storage systems that become in-syncin the future.

In another example, a mediation service (800) may mediate based on aunique key arranged for each potential future race. In such a case, thein-sync storage systems may agree to use a new key. Given that a new keymay not be set atomically on all storage systems at the same time, untilall in-sync storage systems receive and record the new key, all storagesystems should retain their old keys and the values each storage systemattempted to set in any previous mediation attempt. In this example, anyearlier non-raced keys and any earlier key/value mediation attempts maybe circulated between all in-sync storage systems for the pod andrecorded on each such storage system, along with a new key to use forfuture mediation attempts. For each previous non-raced key (notincluding the new key), this exchange may also select a single,agreed-upon value that all systems may use in racing for that key. Afterall in-sync storage systems for a pod have received and recorded all ofthese mediation keys and values (and the new agreed-upon key for anyfuture race), the storage systems in the pod may then agree to discardthe older keys and values in favor of the single new key. Note that twoor more storage systems may have attempted to set the same mediation keyto different values, and all such values may be recorded. If there is afault during the process of exchanging or receiving all these mediationkeys and key/value pairs for past mediation attempts, then some storagesystems may not have received and recorded the new mediation keys andvalues, while others might have. If the mediation service (800) becomesavailable before all in-sync storage systems for the pod can reconnectwith each other, then a subset of storage systems for the pod mayattempt to use the mediation service (800) to detach another storagesystem from the pod. To win mediation, a storage system may attempt toset all recorded keys to their recorded values, and if that works, tothen set the new key to a unique value. If more than one value wasrecorded for the same key, then that step succeeds if setting any one ofthose values is successful. If the first step (setting previous keys)fails or the second step (setting the new key to the new unique value)fails, then the storage systems participating in that attempt atmediation may go offline (retaining the value it attempted to set forthe new key). If both steps succeed, then the communicating storagesystems may detach the non-communicating storage systems and continueserving the pod. As an alternative to exchanging all past keys andvalues, a storage system may record only the keys and values that ittries, with no exchange of keys and values from other storage systemsfor a pod. Then, if an in-sync storage system reconnects with otherin-sync storage systems for a pod (where none had succeeded ininteracting with a mediation service), the in-sync storage system mayexchange one new mediation key, and then exchange an acknowledgment thatthey both received and recorded the agreed upon new key. If a faultprevents exchanging the acknowledgment, then a future attempt atmediation (to a now-available mediation service) by a storage systemthat had never received the new key may attempt to reassert its previouskeys and values. A storage system that had received the new key but hadnot received an indication that all storage systems for the pod hadreceived the key may assert its previous mediation keys as well asasserting a value for the new key, previous keys first, then the newkey. That future mediation attempt may still fail, and then the storagesystem may again reconnect to other in-sync storage systems and mayagain incompletely exchange new keys, leading to another key. This addsanother key. As keys build up over time with a set of incompleteexchanges of new keys, future mediation attempts by a storage system mayreassert each of its keys, along with any values it previously assertedfor those keys, in the order that they were recorded, until itsuccessfully asserts a value for all keys, or it encounters a failure toassert a key at which point it stops asserting keys and goes offline.

In another example, a new mediation service may be configured when acurrent mediation service is unavailable. For example, if all in-syncstorage systems for a pod are communicating with each other, but are notin communication with the current mediation service, then the pod may beconfigured with a new mediation service. This is similar to the previousalgorithm of selecting a new key or new mediation values, but the newkey is further configured to use a new mediation service rather thanmerely being another key associated with the same service. Further, ifthere is a fault during this operation, as with the previous algorithm,some systems may race for older keys, and so systems that know both theold keys and the new key with the new mediation service may race for thenew key on the new mediator service. If the previous mediation serviceis permanently unavailable, then all in-sync storage systems shouldeventually reconnect with each other and complete the exchange of thenew mediation service and any keys and values associated with the newmediation service before pod service can be resumed safely.

In another example, a model for resolving faults may be to implementpreference rules to favor one storage system over other storage systems.In this example, if a preferred storage system is running, it staysrunning and detaches any storage systems it is not communicating with.Further, any other system that is not in proven communication with thepreferred system takes itself offline. In this example, when anon-preferred storage system eventually reconnects with a preferredstorage system, then if the preferred storage system had not yetdetached the reconnecting storage system, then the two storage systemsmay recover and resume from the state of both storage systems beingin-sync, whereas if the preferred storage system had detached thereconnecting storage system then the reconnecting storage system must beresynchronized first to get it in-sync for the pod before it can resumeservicing the pod. Having, a preferred storage system may not be asuseful for providing high availability, but may be useful for other usesof synchronous replication, particularly asymmetric synchronousreplication. Take for example, the case of mirroring a pod from acentral, large storage system in a data center or campus, to a smaller(perhaps less managed) storage system running closer to applicationservers, such as in top-of-rack configurations. In this case, it may bebeneficial to always favor the larger, more managed central storagesystem in cases of network failures or when the top-of-rack storagesystem fails, while bringing down service for a pod altogether if thecentrally managed storage system fails. Such top-of-rack storage systemsmight be used only to improve read performance or to reduce load ondata-center storage networks, but if asynchronous replication or otherdata management services are running only on the centrally managedsystem, it may be preferable to reroute traffic to the central storagesystem or stop servicing and call tech support than to allow thetop-of-rack storage system to continue alone. Further, preference rulesmay be more complex—there may be two or more such “preferred” storagesystems coupled, perhaps, with some number of additional storage systemsthat rely on the preferred or required storage systems. In this example,the pod is online if all the preferred or required storage systems arerunning, and is down if some of them are not running. This is similar toa quorum model where the size of the quorum is the same as the number ofvoting members, but it is simpler to implement than a generalized quorummodel that allows for fewer than all voting members.

In another example, a combination of mechanisms may be used, which maybe useful when a pod is stretched across more than two storage systems.In one example, preference rules may be combined with mediation. In thetop-of-rack example, the larger central storage system in a data centeror campus might itself be synchronously replicated to a large storagesystem in a second location. In that case, the top-of-rack storagesystems may never resume alone, and may prefer any of the larger centralstorage systems in the two locations. The two larger storage systems inthat case might be configured to mediate between each other, and anysmaller storage systems that can connect to whichever of the two largerstorage systems that remain online may continue servicing their pod, andany smaller storage systems that cannot connect to either of the twolarge storage systems (or that can only connect to one which is offlinefor the pod) may stop servicing the pod. Further, a preference model mayalso be combined with a quorum-based model. For example, three largestorage systems in three locations might use a quorum model between eachother, with smaller satellite or top-of-rack storage systems lacking anyvotes and working only if they can connect to one of the larger in-syncstorage systems that are online.

In another example of combining mechanisms, mediation may be combinedwith a quorum model. For example, there may be three storage systemsthat normally vote between each other to ensure that two storage systemscan safely detach a third that is not communicating, while one storagesystem can never detach the two other storage systems by itself.However, after two storage systems have successfully detached a thirdstorage system, the configuration is now down to two storage systemsthat agree they are in-sync and that agree on the fact that the thirdstorage system is detached. In that case, the two remaining storagesystems may agree to use mediation (such as with a cloud service) tohandle an additional storage system or network fault. This mediation andquorum combination may be extended further. For example, in a podstretched between four storage systems, any three can detach a fourth,but if two in-sync storage systems are communicating with each other butnot to two other storage systems they both currently consider to bein-sync, then they could use mediation to safely detach the other two.Even in a five storage system pod configuration, if four storage systemsvote to detach a fifth, then the remaining four can use mediation ifthey are split into two equal halves, and once the pod is down to twostorage systems, they can use mediation to resolve a successive fault.Five to three might then use quorum between the three allowing a drop totwo, with the two remaining storage systems again using mediation ifthere is a further failure. This general multi-mode quorum and mediationmechanism can handle an additional number of situations that neitherquorum between symmetric storage systems nor mediation by itself canhandle. This combination may increase the number of cases where faultyor occasionally unreachable mediators can be used reliably (or in thecase of cloud mediators, where customers may not entirely trust them).Further, this combination better handles the case of three storagesystem pods, where mediation alone might result in a first storagesystem successfully detaching a second and third storage systems on anetwork fault affecting just the first storage system. This combinationmay also better handle a sequence of faults affecting one storage systemat a time, as described in the three to two, and then to one example.These combinations work because being in-sync and a detach operationresult in specific states—in other words, the system is stateful becauseit is a process to go from detached to in-sync, and each stage in asequence of quorum/mediator relationships ensures that at every pointall online/in-sync storage systems agree on the current persistent statefor the pod. This is unlike in some other clustering models where simplyhaving a majority of cluster nodes communicating again is expected to beenough to resume operation. However, the preference model can still beadded in, with satellite or top-of-rack storage systems neverparticipating in either mediation or quorum, and serving the pod only ifthey can connect to an online storage system that does participate inmediation or quorum.

In some examples, a mediation service (800), or external pod membershipmanagers, may be located in fault zones that are different than faultzones for the synchronously replicated storage systems (814, 824). Forexample, with a two storage system pod (430), if the two storage systems(814, 824) are separated into distinct fault zones by, for example,physical location—one in a city and the other in the outskirts of thecity, or one in a data center connected to one power grid or Internetaccess point and the other in another data center connected to adifferent power grid or Internet access point—then it is generallypreferable to be in some other fault zone than the two storage systems.As one example, the mediation service (800) may be in a different partof the extended urban area of the city, or connected to a differentpower grid or Internet access point. However, synchronously replicatedstorage systems may also be within a same data center to provide betterstorage reliability, and in this case, network, power, and cooling zonesmay be taken into account.

The example method depicted in FIG. 8 includes requesting (802), by afirst storage system (814) in response to detecting a triggering event,mediation from a mediation service (800). In this example, a triggeringevent may be a communication fault in the data communications link (816)between the first storage system (814) and the second storage system(824), where detecting the fault may be based on a hardware failureinitiating an interrupt, based on a failure to acknowledge atransmission, or based on failed retry efforts, or through some othermethod. In other cases, a triggering event may be expiration of asynchronous replication lease, and requesting mediation may be part ofattempting to coordinate synchronizing the connection and resuming ofactivity leases. Such a lease may initially be established in dependenceupon the timing information for at least one of the plurality of storagesystems in a variety of different ways. For example, the storage systemsmay establish a synchronous replication lease by utilizing the timinginformation for each of the plurality of storage systems to coordinateor exchange clocks. In such an example, once the clocks are coordinatedfor each of the storage systems, the storage system may establish asynchronous replication lease that extends for a predetermined period oftime beyond the coordinated or exchanged clock values. For example, ifthe clocks for each storage system are coordinated at time X, thestorage systems may each be configured to establish a synchronousreplication lease that is valid until X+2 seconds. A further explanationfor coordinating or exchanging clocks may be found within U.S.Provisional Application 62/518,071, which is incorporated by referenceherein in its entirety.

Further, requesting (802), by the first storage system (814) in responseto detecting the triggering event, mediation from the mediation service(800) may be implemented by a controller of the first storage system(814) detecting a triggering event and sending a request (860) over anetwork (854) to a mediation service (800). In some examples, amediation service (800) may be a third party service that provides—tomultiple computer systems—mutually exclusive access to a resource, suchas a particular database entry for storing a value. For example, themediation service (800) may be provided by a database service providedby a cloud service provider, provided by a host computer issuingrequests to modify the dataset, or by some third party service providingmutually exclusive access to a resource, where the resource may bestorage, a state machine, or some other type of resource capable ofindicating a particular modification based on a request from aparticular client. In this example, after sending the request (860) formediation, the first storage system (814) waits (803A) for an indicationfrom the mediation service (800) that indicates a positive mediationresult (803B) or a negative mediation result or lack of response (803C).If the first storage system (814) receives a negative mediation resultor receives no response (803C), and if a threshold amount of time towait has not been exceeded, then the first storage system (814) maycontinue (806) to wait more time. However, if the amount of time waitingexceeds the threshold amount, then the first storage system (814) maycontinue (806) by determining that another computer system wonmediation, and taking itself offline. In some examples, as discussedabove, a request for mediation may be received by the mediation service(800) as an atomic compare-and-set operation that attempts to set avalue for a shared resource (852) that may also be the target of acompare-and-set operation received from another of the storage systemsmaintaining the pod (430), where the storage system that successfullysets the shared resource (852) wins mediation.

The example in FIG. 8 also includes the second storage system (824)requesting (810), in response to detecting a triggering event, mediationfrom the mediation service (800). Requesting (810), in response todetecting a triggering event, mediation from the mediation service (800)may be implemented similarly to the implementation of requesting (802),in response to the triggering event, mediation on the first storagesystem (814). However, in this example, the second storage system (824),in response to sending a request (862) to the mediation service,may—contrary to the mediation success of the first storage system(814)—receive a failure message, or some indication that the request(862) for mediation was not successful.

The example method in FIG. 8 continues by (804), in the event that anindication (864) of a positive mediation result is received by the firstcomputer system (814), responsive to the indication (864) of thepositive mediation result from the mediation service (800), the firstcomputer system (814)—instead of the second storage system(824)—processing (804) data storage requests directed to a dataset (426)that is synchronously replicated across the first storage system (814)and the second storage system (824). Synchronous replication of adataset (426), which implements a pod (430), in addition to receivingand processing data storage requests directed to a dataset (426) may beimplemented as described with reference to FIGS. 8A and 8B of U.S.Provisional Applications 62/470,172 and 62/518,071, which areincorporated herein in their entirety. In this example, as describedearlier with reference to FIG. 8 , responsive to an indication (864) ofa positive mediation result, the first storage system (814) may beconsidered the storage system that wins mediation, and the first storagesystem (814) may detach the storage system with which communication waslost. However, in other examples, mediation may be implemented accordingto any of the other described methods of mediation, or combinations ofmethods of mediation.

In some examples, defining a preference for which storage system among aplurality of storage systems synchronously replicating a dataset (426)is to win mediation may be implemented by specifying a delay value foreach of the plurality of storage systems. For example, if a firststorage system (814) is designated as a preferred storage system, thenthe first storage system (814) may be assigned a delay value of zero (0)before making a request for mediation from the mediation service.However, for non-preferred storage systems, a delay value may beassigned to be greater than zero, such as 3 seconds, or some other valuethat would generally result in the preferred storage system winningmediation simply due to a loss of communications between synchronouslyreplicated storage systems.

For a further explanation of mediation solutions, the following examplesextend the techniques for implementing mediation described within U.S.Application Serial Nos. 62/470,172 and 62/518,071, which areincorporated herein in their entirety, including but not limited to,implementations regarding “pods” for a representation of a particulardataset synchronously replicated between some number of storage systems,“membership” as a term for the storage systems that nominally holdsynchronous replicas of a particular pod, “in-sync” for a member's copyof a dataset that is considered up-to-data with respect to a pod'sdataset, and “online” for a storage system that is ready for activelyserving the contents of a pod.

As described in the above-referenced applications, a storage system mayimplement a set of models for discovering and responding to faultswithin and between the storage systems that are synchronouslyreplicating data—where such an implementation may ensure thatsplit-brain operation does not occur, which may render a synchronouslyreplicated data set vulnerable to data corruption. Further, theembodiments in the above-referenced applications describeimplementations that include: defining preferences; mediation; quorumpolicies; how subsets of storage systems being associated with a pod maydelay or not automatically trigger mediation in order to code softpreferences; models for updating mediators; altering preferences; andswitching from quorum models to preference models or mediation models asstorage systems fail incrementally. As described below, one or more ofthese implementations may serve as a basis for additionalimplementations.

In some implementations, a storage system may make adjustments inresponse to unavailable mediators. For example, a storage systemimplementing a pod that is configured to use mediation to respond tofaults should agree on a mediation service and agree on a set ofmediation parameters to be used for mediation—such as the key that willbe used when performing mediation. Further, a mediation service and themediation parameters may be altered such as through use of a model thattransitions through intermediate steps that make use of both an earlierand a subsequent mediator service and parameters prior to transitioningto use of a single, subsequent mediation service and mediationparameters. In some implementations, these example alterations may beperformed safely through to completion by a storage system without thestorage system communicating with a mediation service given that atleast some of the storage systems are able to communicate with eachother. For example, if one or more storage systems that are configuredfor mediation stop communicating, such as due to a storage system faultor network fault, then mediation—possibly including use of multiplemediator services or mediation parameters—may be used to proceed furtherunless communication is restored.

In some implementations, if a mediator service is unavailable at thetime of a network or storage system fault, then operation of a pod mayfail or may pause until the mediator service becomes available again oruntil the network or storage system fault is repaired or communicationis otherwise restored. In some examples, for an individual storagesystem case, pod failure may depend on failure of both a storage systemwithin the pod and of the mediator service (or at least of access to themediator service)—this may be considered a low-probability,double-failure event. However, if there are a large number of storagesystems and a large number of pods, where those pods use the samemediator service, then a probability of a mediator service failureleading to a failure of at least one pod increases substantially. Insuch an example, this issue of increased probability of failure may besolved using the capability for run-time switching of mediator servicesby mediation-enabled storage systems for a pod by storage systems in thepod monitoring their ability to communicate with various mediatorservices, and switching mediator services for the pod if any issuesarise, where a goal may be to complete a switch before any issue arisesthat may require mediation and while the online storage systems for apod are still communicating with each other. In some examples, switchingto a new mediator service may be performed quickly; if, for example,mediator service access is checked periodically or responsive to amonitoring event—where an example period may be once every 30 secondsand where a monitoring event may include a change in network status—themediator service may be switched responsive to one or more failed healthchecks or to one or more monitoring events. Continuing with thisexample, responsive to a negative periodic check and/or a monitoringevent, then any pods using the particular mediator service may switch toan alternative solution for mediation, where the switch may in someexamples occur within about one minute of a mediator service becomingunavailable or unreachable. In this examples, such monitoring orresponsiveness to monitoring events may greatly reduce the window ofvulnerability for any of pod failing due to a dual failure that includesa mediator service failure.

In some implementations, mediator services may be implemented onphysical servers or virtual servers, where a given mediator service maybe internally clustered or may be distributed for high availability withmultiple network addresses. In other cases, a mediator service may be asimple, one-node service that does not use any internal form of highavailability. In some examples, mediator services may also beimplemented within a public cloud, such as Amazon Web Services™,Microsoft Azure™, AlibabaCloud™, among others, or a mediator service maybe implemented in vendor-provided and managed data centers. In otherexamples, a mediator service may be implemented using a database serviceto store keys using atomic primitives such as an exclusive create oratomic compare and swap.

In some implementations, a particular mediator service may be describedaccording to a mediator type, method of access, or types of mediatorprimitives offered, in addition to being described according to how adetermination is made for naming and accessing a mediator service over anetwork. For example, a mediator service may provide a list of InternetProtocol addresses, one or more names that may be looked up using DNS,or a service name that may be looked up in other types of directories orusing other directory services. Further, some existing web storage ordatabase services that are provided by cloud services may directlyimplement mediator services based on providing operations such asconditional PUT requests, such as conditional PUT requests in Amazon™ S3or some conditional database operations such as those provided byDynamoDB™. In these cloud services examples, the naming may be a bucketor database name associated with a customer account.

In some implementations, a set of storage systems or pods may beconfigured with a set of possible mediator services. In some examples,alternatively, or additionally, storage systems, or pods, may beconfigured to use intermediate mediator service brokers that may providelists of possible mediator services, where those lists may also changeover time—for example, a list may be updated to include new mediatorservices, or to remove or replace mediator services that may be obsoleteor that may be scheduled for servicing or to be taken offline. As anexample, a vendor may provide a mediator broker service that may providestorage systems with sets of public cloud-based mediator services thatare scattered around and between different availability zones, such asthose provided by AWS™, scattered around and between differentgeographic regions, or even scattered between multiple different cloudservice providers. Further, a vendor may add a set of mediator servicesrunning on their own infrastructure in addition to meditator servicesprovided through public cloud services. In some examples, as analternative, a local IT service or department may deploy both storagesystems implementing synchronously replicated data and also localmediator services, such as a local mediator service operating in a localvirtual machine, where the local mediator services may be installed at avariety of locations that are managed by the local IT service ordepartment. Further, in some examples, combinations of environments maybe used in implementing mediator services, such as where a localmediator service may serve a local storage system that is synchronouslyreplicating data, where local mediation is provide instead of acloud-based meditator service—where a local mediator service may beintegrated with a vendor- or cloud-operated service to provide links tolocal mediator service instances back to local storage systems from thevendor- or cloud-operated service in order to avoid needing to configureall storage systems to communicate with a local broker.

In some implementations, storage systems configured to use mediationmust agree on which mediator service to use, agree on what events orcircumstances are to occur prior to mediation, and agree on mediationparameters when invoking a mediator service. Further, during a change inmediator service or mediation parameters for a storage system pod, theremay be a discrepancy in mediator configuration between storage systemsimplementing a pod; however, such differences may be transitional ortemporary.

In some implementations, a list of mediator services, such as the listdescribed above, may be coupled with a list of proposed mediatorservices that are determined by mediator service brokers, where theresulting list may form a list of potential mediator services that astorage system, or pod, may choose among. For example, if a pod isstretched to include a second or subsequent storage system that isconfigured for mediation for the pod, then one of those mediationservices, and any necessary mediation parameters, may be chosen andcommunicated between those mediation-enabled storage systems. In thisexample, a mediator service should be usable from all of those storagesystems, and if the storage systems are unable to agree on a commonusable mediator service, then a stretch operation might fail or might bedelayed until conditions change and the storage systems are able toagree or reach a consensus.

In some implementations, on an ongoing basis, online mediation-enabledstorage systems for a pod may monitor a currently chosen mediatorservice and potential alternatives. For example, on a periodic oraperiodic basis, one or more storage systems may monitor one or more ofthe available mediator services to determine if a mediator service isunavailable or unreliable. In this example, a given mediator servicefrom among multiple mediator services may be determined to beunavailable if one or more attempts to communicate with the givenmediator service are not successful. Further, in this example, a givenmediator service from among multiple mediator services may be consideredunreliable if it has been unavailable over some previous window of timeeven if the given mediator service is available at a current point intime—particularly if it has been found unavailable but becomes availableand then unavailable again several times over some recent period oftime. In some examples, a mediator service may also be consideredunreliable if it responds unusually slowly to monitor requests.Similarly, a mediator service previously determined to be unreliable maybe upgraded to being reliable if it responds quickly or reliably over asubsequent window of time.

In some implementations, mediator service monitoring could also resultin alerts which could notify an administrator or user, notify the publiccloud provider, or notify the vendor so the mediator service can berepaired or the mediator service broker's mediator service list can beadjusted to include more reliable alternatives.

In some implementations, mediator service parameters and characteristicsmay be used by a storage system pod as a basis for choosing a mediatorservice from among multiple available mediator services. For example, iflocations of pod member storage systems and locations of mediatorservices are known (such as because locations are detected throughnetwork analysis or because location is explicitly configured), thenthis information can also be used for mediator service selection. Forexample, if a locally deployed synchronously replicated storage systemoperates across multiple data centers, and if the storage systems aremarked by a given data center, and the mediators are marked by a givendata center (or if marked storage systems can serve as mediators), thena pod stretched between two or more of the multiple data centers mayautomatically use a mediator service (or a storage system as a mediator)from a data center other than any one of the data centers that the podis stretched to.

In some implementations, as mentioned above, mediation-enabled storagesystems for a pod can monitor and evaluate a current mediation servicefor availability or reliability. For example, a service may bedetermined to be available if it is reachable and responds to monitorrequests. Further, in this example, health or status of a mediatorservice may be monitored by other storage systems or other tools, wherethe health or status may be communicated to storage systems as part of aselection criterion. In this way, with a cloud- or vendor-hostedmediator monitoring service, any storage systems or monitoring servicecould track health or status results for any mediator service from amonga list of available mediator services. In some examples, some otherservice, perhaps integrated with a mediator service broker, couldaccumulate and provide health or status information on request or couldotherwise update storage systems and pods so that storage systems can bemade aware of any issues with a given mediator service. Such amonitoring service is not critical to performing actual mediation, sothis health or status data can be distributed in ways to make it widelyavailable, as long as it is reasonably up-to-date and does not reportresults that are excessively incorrect or otherwise incorrect beyond agiven error tolerance.

In some implementations, a storage system that is synchronouslyreplicating data may implement multiple different mediator preferences.For example, given a list of mediator services to choose from, a set ofstorage systems can determine whether some mediator services are in someway better than others, or they can rule out some mediator servicesaltogether. Further, in some examples, mediator service preferencescould also be defined for a storage system pod or for storage systems,or for a particular IT department or customer explicitly through somemechanism such as setting a value that can be sorted for preference, orby tagging mediator services as preferential for a particular purpose,or for some storage systems or pods.

In some implementations, with regard to mediator parameters, mediatorservice location is one useful parameter, as noted previously. Forexample, if the mediator service location is known, and the location ofa storage system is known, then the location of the mediator service canbe chosen based on a relative location of a given storage system andmediator service—where a nearer, or more quickly accessible, mediatorservice may be preferred over mediator services farther away. Further,in some examples, if storage systems are added to a pod, mediatorservice location can be reevaluated with respect to the added storagesystems. In other examples, information regarding a data center may beanother parameter, where if a pod includes two mediation-enabled storagesystems, and those two storage systems are in separate data centers,then a mediator service may be selected which is in a third data center.In other words, a mediator service may be selected based on beingimplemented in a data center that is independent of the one or more datacenters implementing a storage system pod to ensure greater reliabilityand/or availability of the mediator service in the event that the one ormore data centers implementing the storage system pod is undergoingnetwork faults, power failures, or some other type of data center fault.However, in other cases, if multiple mediation-enabled storage systemsfor a pod are in the same data center, then a mediator service may beselected within the same data center, but where network layout relativeto the mediator service and storage systems may be considered; forexample, a mediator service may be selected such that a communicationfailure between the storage systems is unlikely to coincide with acommunication failure between all mediation-enabled storage systems andthe selected mediator service.

In some implementations, such as in the case of cloud- orvendor-provided mediator services, geography can either be tagged, ornetworks can be probed for response time, network hops, or networkaddress ranges. A cloud or vendor provided mediator service may providegeographic location information without much overhead. However, in thecase of a local storage system implementation for synchronouslyreplicating data, geographic information may be inferred from networkinformation, such as IP addresses or geolocation among other methods.Further, synchronous replication is not generally implemented over verylong distances, therefore storage systems that are members of a pod maytypically be within one geography and are connectable with similarlatency and network hop counts to typical cloud- or vendor-providedmediator services within that geography.

In some implementations, as discussed above, there may be multipleaspects to location information. For example, data center information,network layout information within one data center, power and coolingzone information within a data center, civil infrastructure power gridinformation, data center complex information, urban area descriptions,administrative zone information, controlling government entityinformation, among other information—where mediator services and storagesystems may be tagged with any set or subset of these and other types oflocation information. Further, in some examples, any number of thesevarious aspects of location information may be taken into accountsimultaneously, such that if mediation-enabled storage systems for a podappear to be separated from each other by some particular parameter, amediator service may be chosen to be independent with respect to thatparticular parameter from each of the mediation-enabled storage systems.In some cases, aspects of location information may be prioritized. Forexample, two storage systems may be in distinct administrative zones,but there may not be a third administrative zone, even though there is athird power grid.

In some implementations, with regard to mediator parameters, healthhistory and scheduled downtime may be considered as bases for selectinga mediator service. For example, if planned downtime for a computingenvironment that implements a mediator service may be scheduled inadvance, then a storage system implementing a pod may have time toswitch to a mediator different from the mediator service scheduled fordowntime prior to the downtime—where the storage system may switch backsubsequent to the scheduled downtime, or the storage system may remainwith the switched-to mediator service until another event promptsanother switch.

In some implementations, a factor for selecting a mediator service maybe based on an entity serving as a host for the mediator service. Forexample, locally, an IT department may prefer locally implementedmediator services, but may accept using a cloud- or vendor-providedmediator service if a suitable local mediator service is not availableor is being taken offline, such as for maintenance, or if a suitablethird location is not yet configured. Further, in some examples, avendor may prefer that customers for the vendor use a public cloud-basedmediator service, but may let customers use back-end, vendor-hostedmediator services during public cloud outages or for improving mediationresponse time (such as because a cloud service is under adenial-of-service attack or doesn't have data centers operating in ageography, such as China or Antarctica).

In some implementations, given a list of available and configuredmediator services, from whatever sources (explicit configuration,mediator service brokers, etc.), some mediator services may beeliminated from consideration to serve as mediators based on notmatching or meeting specified requirements (e.g., mediation-enabledstorage systems for a pod are in separate data centers, but a particularmediator service is in one of those data centers), or based on beingscheduled for downtime. In some examples, other mediator services can besorted for priority based on one or more of: health or health history,suitability according to one or more preferences or requirements,scheduled maintenance, geography, network hops to reach a given mediatorservice, network latency to the mediator service, network subnets, amongothers.

In some implementations, with regard to selecting a mediator service, acurrent list of potential mediator services may be evaluated and themost suitable mediator service that matches one or more requirements maybe selected, or selection could match the greatest number of highpriority requirements. As mentioned previously, a list of potentialmediator services can be re-evaluated from time-to-time or responsive toevents corresponding to changes in mediator service status or theaddition of more available mediator services.

In some implementations, future mediation may only be necessary for apod where there is currently more than one active onlinemediation-enabled storage system. In some examples, prior (perhapsmediated) faults may have already resulted in some storage systemsbecoming offline for a pod. In such an example, until one of thosestorage systems comes back online, how the offline storage systems mightrelate to a particular mediator service may not matter. Further, in thisexample, in order for a storage system to come back online for a pod andresume being mediation enabled, the storage system that had been offlinefor the pod may first be required to ensure that it has reliable accessto a suitable mediator service that is shared among the storage systems,and where the mediator service is reliably accessible from the existingonline mediation-enabled storage system for the pod. In this example,this may result in the existing online mediation-enabled storage systemsswitching to a new mediator as part of bringing the additionalmediation-enabled storage system back online. In some examples, adegenerate case is where a fault or a sequence of faults resulted inexactly one mediator-enabled storage system for a pod remaining online,in which case mediation is no longer necessary until anothermediator-enabled storage system comes back online.

In some implementations, where a storage system is not configured to usea mediator service, the storage system may remain online and continue tosynchronously replicate data for a pod, or may rejoin a pod after comingback online after being offline—where the storage systems remain onlineand in-sync based on being able to communicate with other storagesystems. However, in this example, if storage systems are unable tocommunicate with each other, a storage system may remain offline until acommunication link is re-established. Further, in this example where amediator service is not available or where a storage system is notconfigured to use a mediator service, the storage system may implementone or more quorum protocols, as discussed previously. Further, in thisexample, additional storage systems that are not current members of apod may be added to extend the set of storage systems that participatein the one or more quorum protocols.

In some implementations, a storage system may switch to alternativemodels in response to mediator reliability changes or mediatoravailability changes. In this example, a storage system may switch to analternate mediator service in response to a first mediator servicebecoming unavailable or unreliable—which is one response among otherpossible responses. For example, another response to a mediator servicebecoming unavailable or unreliable is to switch cluster availabilitymodels. Continuing with this examples, other availability models weresuggested in the earlier referenced applications, including quorummodels and preference models. In some cases, a particularly applicablealternative model is a preference model because an efficient response toappropriate mediator services becoming unavailable is for storagesystems for a pod to agree on a preferred storage system that willremain online for a pod if a later fault prevents storage systems forthe pod from communicating. In this example, as long as this transitionto a preference model is reliably and sufficiently communicated prior toa fault that would otherwise have been mediated, the preference modelcan operate safely. As a more specific example, for a pod with twomediation-enabled storage systems, if the mediator service is found tobe unavailable or unreliable one of those two storage systems can bechosen, and if the two storage systems subsequently stop communicating,then the preferred storage system may remain online for the maintainingor servicing the pod (given that the preferred storage system isoperating properly) and the other, non-preferred, storage system may gooffline for the pod (whether or not it is operating properly and whetheror not the other storage system is running properly). Concluding thisexample, this situation can be re-evaluated until continued monitoringof mediator services indicates that a suitable, available, andsufficiently reliable mediator service can again be selected for thestorage system.

In some implementations, if there are more than two mediation-enabledstorage systems implementing a pod, and at least two of the storagesystems can still reliably access a common mediator service, but one ormore cannot, then the inability of the additional storage systems toreliably access the mediator service might not have to result inswitching to a preference model or other model because mediating onlybetween those two might be considered sufficiently reliable and betterthan switching to preference model. Continuing with this example, evenin the case of only one mediation-enabled storage system for a pod beingable to access a mediator service, mediation could be left in placeinstead of switching to a preference model, which essentially means thatif there is a failure, only the storage system in communication with themediator service may take over unless and until the situation isremedied. In this example, the storage system in communication with themediator service taking over may be preferable, where even aftercomplete unavailability of any suitable mediator service results in apod switching to a preferred storage system, the pod may switch back toa mediation model after at least one storage system reliably accesses amediator service instead of waiting for multiple storage systems or allstorage systems. In this example, only one storage system being able tomediate is not worse than a preference model given that the mediatorservice remains available and allows for another storage system to winmediation if the situation is repaired—even in the case of all storagesystems going down and some coming back up with repaired mediatorservice connections.

In some implementations, in response to the currently active and onlinemediation-enabled storage system for a pod detecting that some or allprevious reliably available and suitable mediator services for the podare no longer reliable or available, and in response to choosing toswitch to a preference fault handling model, at least until thecondition partially or completely recovers, the storage system for thepod may use a variety of models for deciding whether preference shouldbe assigned to one storage system or another storage system for the pod.In some cases, different models may be selected based on one or morefactors corresponding to: host connectivity, host locations, currentload, network paths or latency between hosts and individual storagecontrollers or network adaptors on individual storage systems within thepod, among other. In other cases, a fallback preference can beexplicitly configured, or it can track available information aboutapplications and services running on hosts and their ability to adapt toloss of access to one storage system or another.

Further, in some examples, responsive to switching from mediatedavailability to preferred availability, the change can be communicatedto a host-side device, application, or data center managementinfrastructure that may result in some applications or services beingmoved around to match the new storage system preferences. In othercases, some applications or services could be configured so that whenthey have access issues, they will also switch themselves to thelocations or access paths associated with the preferred storage systemfor the pod. In this way, in this example, this bi-directionalcommunication with other applications and services that depend onstoring data can be beneficial to the handling those applications andservices.

In some implementations, a storage system for a pod may implementvarious techniques for managing disaster recovery. For example, a modelfor configuring hosts, applications, and storage systems for disasterrecovery is to have a set of storage systems and servers that runtogether for a set of applications and services in a first location, butwhere procedures are in place to bring up storage systems, applications,and services together in a second location when there is a sufficientlycatastrophic fault that the first location can no longer serve criticalapplications and services (floods, extended power grid failure, andterrorism are commonly cited examples, but more common ones are humanerror and network isolation). In some implementations, a technique forhandling this situation is to depend on some higher-level monitoring andhigh-availability service to migrate storage accessibility from thefirst location to the second location together with applications andservices, and to do all of the migration in response to the higher-levelmonitoring and high-availability service determining that such amigration is necessary. In some environments, this change may betriggered by human intervention via a control interface instead ofautomatically.

In some implementations, storage is a particularly critical aspect ofaccess migration because storage is particularly subject to becomingcorrupt or unrecoverable if services are accidentally running in twolocations. One example for handling services running in two locationswith less danger of corruption is to use snapshots in response to higherlevel service triggering migrating storage access to a second location.In this model, a storage system at the second location for a pod mightbe configured such that it will not mediate, participate in quorum, orprefer itself to take over the pod. In this example, if a pod includesonly two storage systems, where one storage system is at a firstlocation and another storage system is at a second location, then thefirst location may be preferred. Continuing with this example, the firstlocation (or a first location and some third location) might otherwisecomprise multiple storage systems for the pod, which may be configuredto mediate or use quorum between them (or might prefer a subset of thosestorage systems), but storage systems at the second location would notparticipate in a quorum, and would become offline in case of faults thatprevent the storage systems at the second location from communicatingwith other online, in-sync storage systems for the pod.

Continuing with this example, in this model, storage takeover in thesecond data center might involve making a logical copy of any pods toform new pods, where the new pods and storage entities (volumes, filesystems, object stores, etc.) have sufficiently different identitiesthat the logical copy of each such pod is not confused by hosts,applications, or services with any such storage entities and pods thatmight still be running in the first (or other) location (or locations).Further, example models for copying a pod, including logically copying apod on a storage system that is currently offline for the pod, areincluded within U.S. Patent Application No. 62/518,071, which isincorporated herein in its entirety.

In some examples, if a network fault or communication failure recovers,then if the original pod can come back online for the storage systemsamong the first and second locations, then at that time, the logicalcopy of the pod can be logically copied back to the original pod as partof a coordinated fail-back operation which first ensures that affectedhosts, applications, and services are offline in the first location (andother locations) before logically copying back the pod. In this example,this may, for example, make temporary use of asynchronous or periodicreplication, or may use models such as synchronous replication resyncmethods to bring the original pod up-to-date prior to failing back thehosts, applications, and services to the first location (and possiblyother locations). Further, in this example, prior to logically copyingback to the original pod, a snapshot might be taken of that pod toensure that updates are not lost.

Continuing with this example, logical copies of pods in a secondlocation may also be used for testing, such as part of a procedure toensure that all of a critical dataset is properly replicated to thesecond location, or for other uses of a logical copy of a data set suchas running reports or for using copies of production data fordevelopment or research purposes that are logically isolated fromproduction locations. Further, in some cases, identities that might haveto be changed as part of making and presenting a suitable copy of a podcan include fibre channel identifiers, network addresses, iSCSI IQNs,SCSI serial numbers, volume identifiers, pod identifiers, file systemunique identifiers, file system export names, or object pool/bucketidentifiers. In some cases, names can be kept the same even asunderlying identifiers are changed (which can be enough to ensure, say,that a multipathing disk driver will not be confused). In other cases,names might be changed as well, such as because takeover scripts andprocedures are written to use new names for storage-related entities aspart of performing a takeover.

In some implementations, a storage system may apply different models topod-spanning consistency groups. For example, in some cases, aconsistent dataset may comprise more than one pod, or might span podsthat operate on distinct or overlapping sets of storage systems. In thisexample, if properly maintaining a data set that spans pods should takelocation into account when preserving access to storage systems as partof fault handling, such as because a set of application components andservices store data to more than one storage system within each locationfor a set of pods, then some technique may be required to ensure thatautomated online/offline decisions are location consistent for multiplepods as well as for multiple storage systems for those multiple pods. Insuch cases, storage systems in one location may coordinate mediation,choices of mediator servers, quorum handling, or any switching frommediation to fault preference.

Continuing with this example, in one case, a first set of storagesystems S₁, S₂, and S₃ at a first location L₁ might be members of podsP₁, P₂, and P₃, respectively, with a second set of storage systems S'₁,S'₂, and S'₃ at a second location L₂ also being members of pods P₁, P₂,and P₃, respectively. In this example, a set of applications andservices currently running on hosts in location L₁ might expect to bemigrated as a group to hosts in L₂ if a fault makes that necessary. Insuch cases, the location of storage systems that remain online for allthree pods due to a sequence of faults may need to be consistent betweenall three pods. For example, in the case of a network fault thatdisrupts communication between locations L₁ and L₂, all three pods mustswitch to either S₁, S₂, and S₃ at location L₁, or to S'₁, S'₂, and S'₃at location L₂, and cannot otherwise mix their results. Further, if onestorage system at the first location, say S₁ at location L₁, faults,then all three pods can remain online at location L₂, but if there is asecond fault involving a storage system at the second location, say S'₃,then all three pods may have to go offline at both locations until thesituation is repaired because neither location has a complete dataset.Implementations might support S₂, and S₃ either going offline orremaining online after only S₁ faults in the previous example.

Further, in the case of preference models, the preferred storage systemsfor all three pods can be configured identically, for example with eachpod preferring the storage systems in the first location. This solutionmay work as long as faulting of a first storage system in the secondlocation is not expected to result in the second and third storagesystems in the second location to also go offline.

In some implementations, mediation or quorum models may be morecomplicated. For example, in the case of mediation or quorum models,pods may be unable to make decisions separately from each other if thoseseparate decisions may result in different outcomes. In general, it isnot by itself enough for pods to merely share the same mediator serviceor the same quorum configuration. In the case of a mediator service, itis possible that a key and a first value could be shared between storagesystems at the first location, while the same key and a distinct secondvalue could be shared between storage systems at the second location,such that a first storage system mediating successfully on a failure mayensure that only other storage systems at the same location couldsucceed because they mediate using the same key and value. However,reliably switching mediators is more complicated in such a model, andafter one successful use of mediation within one pod, subsequent uses ofmediation to handle subsequent faults requires a new key/valuecombination (depending on the mediation model), which would then have tobe exchanged again.

In some implementations, as an alternative to the previous example,storage systems for stretched pods in a first location may agree on asingle storage system to engage in mediation or to engage in quorumprotocols for those storage systems in that first location, where thosestorage systems are expected to handle faults consistently for thestretched pods. Further, in this example, storage systems at otherlocations could likewise agree on a particular one of their storagesystems to engage in mediation or to engage in quorum protocols onbehalf of those other locations.

In some implementations, storage systems at a location could use any ofseveral techniques for coordinating which storage system will mediate orengage in quorum protocols, where in some cases, a storage system may beassigned to do this. For example, storage systems could vote betweenthemselves. In other examples, storage systems within a location may usemediation between themselves to a mediator service running within thelocation or through a cloud service or vendor provided service todetermine who can mediate on behalf of the location. In general, anymodel that can arrive at a single, agreed upon storage system to be usedat any one time may work. In some cases, a protocol for changingmediators is an example of a model that could be used for adjusting tosuch changes.

Generally, a computing device other than a storage system may engage inmediation or a quorum protocol on behalf of storage systems at alocation that should operate consistently together. In some examples,instead, there could be a service running somewhere at a location thathandles consistent multi-location coordination for that location. Inthis way, in this example, storage systems that rely on coordinatedconsistency related to a shared location on behalf of a set of pods maybe registered to coordinate through that service. In this example, theservice may use high availability protocols, or mediation or quorumwithin the location to manage itself, and the service could also use areliable distributed agreement model at the time of mediation or quorumexchange, such as through using a PAXOS or RAFT-based model within thelocation. In some cases, this process could make use of a sufficientlyreliable cluster shared file system running within the location.

For further explanation, FIG. 9 sets forth a flow chart illustratingsteps for a storage system to switch between different fault responsemodels according to some embodiments of the present disclosure. Althoughdepicted in less detail, the storage system (814) depicted in FIG. 9 maybe similar to the storage systems described above with reference toFIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, and FIGS. 4-8 , or anycombination thereof. In fact, the storage systems (814) depicted in FIG.9 may include the same, fewer, or additional components as the storagesystems described above.

In the example method depicted in FIG. 9 , in an initial state, astorage system (814) may have one or more communication links that areoperational between one or more storage systems and between a mediatorservice (952), where at least some of the storage systems are configuredto request mediation from a mediator service (952). In other words, asdiscussed above, in some examples, some, but not all, of the storagesystems that are synchronously replicating a dataset are mediationenabled.

The example method depicted in FIG. 9 includes determining (904), amongone or more of a plurality of storage systems, a change in availabilityof the mediator service (952), where one or more of the plurality ofstorage systems are configured to request mediation from the mediatorservice in response to a fault. Determining (904), among the one or moreof the plurality of storage systems, the change in availability of themediator service (952) may be implemented as described above with regardto different techniques for determining when a mediator service is nolonger responding, is no longer reachable, or has begun to respondoutside of a threshold response time.

The example method depicted in FIG. 9 also includes communicating (906),among the plurality of storage systems and responsive to determining thechange in availability of the mediator service (952), a fault responsemodel to be used as an alternate to the mediator service (952) among theone or more of the plurality of storage systems. Communicating (906),among the plurality of storage systems and responsive to determining thechange in availability of the mediator service (952), the fault responsemodel to be used as an alternate to the mediator service (952) among theone or more of the plurality of storage systems may be implemented asdescribed above with regard to different techniques for switching from afault response model that is based on a mediator service to a faultresponse model that is established among one or more of the storagesystems that are synchronously replicating data.

In this way, in the event that a mediator service (952) that iscurrently an agreed-upon mediator service becomes unreliable to mediatebetween the storage systems, a storage system may switch to an alternatemediation model before the current mediator service (952) completelyfails instead of simply becoming less reliable. In other words, in someexamples, if a mediator completely fails or becomes less reliable, andgiven that the storage systems are in communication with each other,then the storage systems may coordinate either a change of mediatorservice or a change to a different fault response model—where given thatthis change is coordinated prior to a failure of a storage system ornetwork interconnect between storage systems, the pod may continue to beavailable without any interruptions. In short, in some examples, inanticipation of a failure, the storage systems implementing a pod maypre-emptively switch between fault response models before a fault toavoid pod unavailability.

For further explanation, FIG. 10 sets forth a flow chart illustratingsteps for a storage system to switch between different fault responsemodels according to some embodiments of the present disclosure. Althoughdepicted in less detail, the storage system (814) depicted in FIG. 10may be similar to the storage systems described above with reference toFIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, and FIGS. 4-8 , or anycombination thereof. In fact, the storage systems (814) depicted in FIG.9 may include the same, fewer, additional components as the storagesystems described above.

The flow chart depicted in FIG. 10 is similar to the flow chart depictedin FIG. 9 in that the flow chart depicted in FIG. 10 includes:determining (904), among one or more of a plurality of storage systems,a change in availability of the mediator service (952), where one ormore of the plurality of storage systems are configured to requestmediation from the mediator service in response to a fault; andcommunicating (906), among the plurality of storage systems andresponsive to determining the change in availability of the mediatorservice (952), a fault response model to be used as an alternate to themediator service (952) among the one or more of the plurality of storagesystems.

However, the flow chart depicted in FIG. 10 —which depicts an examplewhere the alternate fault response model is an alternate mediatorservice—also includes selecting (1002), in dependence upon one or morefactors (1052), the alternate mediator service (1052) from among a listof mediator services (1054). Selecting (1002), in dependence upon one ormore factors (1052), the alternate mediator service (1052) from amongthe list of mediator services (1054) may be implemented above asdiscussed with respect to selecting alternate mediator services fromamong a list of mediators. Further, as discussed above in greaterdetail, the one or more factors may include one or more of: geographicproximity, reliability information, network hops to reach a givenmediator service, communication response time, availability zone,pre-defined priority information, administrative zone information, datacenter complex information, data center information, network layoutbetween the storage system and a given mediator service, urban areadescription of the mediator service implementation, or power gridinformation powering a given mediator service.

For further explanation, FIG. 11 sets forth a flow chart illustratingsteps for a storage system to switch between different mediation modelsaccording to some embodiments of the present disclosure. Althoughdepicted in less detail, the storage system (814) depicted in FIG. 10may be similar to the storage systems described above with reference toFIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, and FIGS. 4-8 , or anycombination thereof. In fact, the storage systems (814) depicted in FIG.9 may include the same, fewer, additional components as the storagesystems described above.

The flow chart depicted in FIG. 11 is similar to the flow chart depictedin FIG. 10 in that the flow chart depicted in FIG. 11 includes:determining (904), among one or more of a plurality of storage systems,a change in availability of the mediator service (952), where one ormore of the plurality of storage systems are configured to requestmediation from the mediator service in response to a fault; andcommunicating (906), among the plurality of storage systems andresponsive to determining the change in availability of the mediatorservice (952), a fault response model to be used as an alternate to themediator service (952) among the one or more of the plurality of storagesystems and selecting (1002), in dependence upon one or more factors(1052), an alternate mediator service (1052) from among a list ofmediator services (1054).

However, the flow chart depicted in FIG. 11 also includes switching(1102), responsive to determining a reliable connection to the alternatemediator service (1052), from the subset of storage systems for handlingmediation to the alternate mediator service (1052) for handlingmediation. Switching (1102), responsive to determining a reliableconnection to the alternate mediator service (1052), from the subset ofstorage systems for handling mediation to the alternate mediator service(1052) for handling mediation may be implemented above as discussedabove with respect to switching to an alternate mediator service.

Readers will appreciate that the methods described above may be carriedout by any combination of storage systems described above. Furthermore,any of the storage systems described above may also pair with storagethat is offered by a cloud services provider such as, for example,Amazon™ Web Services (‘AWS’), Google™ Cloud Platform, Microsoft™ Azure,or others. In such an example, members of a particular pod may thereforeinclude one of the storage systems described above as well as a logicalrepresentation of a storage system that consists of storage that isoffered by a cloud services provider. Likewise, the members of aparticular pod may consist exclusively of logical representations ofstorage systems that consist of storage that is offered by a cloudservices provider. For example, a first member of a pod may be a logicalrepresentation of a storage system that consists of storage in a firstAWS availability zone while a second member of the pod may be a logicalrepresentation of a storage system that consists of storage in a secondAWS availability zone.

To facilitate the ability to synchronously replicate a dataset (or othermanaged objects such as virtual machines) to storage systems thatconsist of storage that is offered by a cloud services provider, andperform all other functions described in the present application,software modules that carry out various storage system functions may beexecuted on processing resources that are provided by a cloud servicesprovider. Such software modules may execute, for example, on one or morevirtual machines that are supported by the cloud services provider suchas a block device Amazon™ Machine Image (‘AMP’) instance. Alternatively,such software modules may alternatively execute in a bare metalenvironment that is provided by a cloud services provider such as anAmazon™ EC2 bare metal instance that has direct access to hardware. Insuch an embodiment, the Amazon™ EC2 bare metal instance may be pairedwith dense flash drives to effectively form a storage system. In eitherimplementation, the software modules would ideally be collocated oncloud resources with other traditional datacenter services such as, forexample, virtualization software and services offered by VMware™ such asvSAN™. Readers will appreciate that many other implementations arepossible and are within the scope of the present disclosure.

Readers will appreciate that in situations where a dataset or othermanaged object in a pod is retained in an on-promises storage system andthe pod is stretched to include a storage system whose resources areoffered by a cloud services provider, the dataset or other managedobject may be transferred to the storage system whose resources areoffered by a cloud services provider as encrypted data. Such data may beencrypted by the on-promises storage system, such that the data that isstored on resources offered by a cloud services provider is encrypted,but without the cloud services provider having the encryption key. Insuch a way, data stored in the cloud may be more secure as the cloud hasno access to the encryption key. Similarly, network encryption could beused when data is originally written to the on-premises storage system,and encrypted data could be transferred to the cloud such that the cloudcontinues to have no access to the encryption key.

Through the use of storage systems that consist of storage that isoffered by a cloud services provider, disaster recovery may be offeredas a service. In such an example, datasets, workloads, other managedobjects, and so on may reside on an on-premises storage system and maybe synchronously replicated to a storage system whose resources areoffered by a cloud services provider. If a disaster does occur to theon-premises storage system, the storage system whose resources areoffered by a cloud services provider may take over processing ofrequests directed to the dataset, assist in migrating the dataset toanother storage system, and so on. Likewise, the storage system whoseresources are offered by a cloud services provider may serve as anon-demand, secondary storage system that may be used during periods ofheavy utilization or as otherwise needed. Readers will appreciate thatuser interfaces or similar mechanisms may be designed that initiate manyof the functions described herein, such that enabling disaster recoveryas a service may be as simple as performing a single mouse click.

Through the use of storage systems that consist of storage that isoffered by a cloud services provider, high availability may also beoffered as a service. In such an example, datasets, workloads, othermanaged objects, that may reside on an on-premises storage system may besynchronously replicated to a storage system whose resources are offeredby a cloud services provider. In such an example, because of dedicatednetwork connectivity to a cloud such as AWS Direct Connect,sub-millisecond latency to AWS from variety of locations can beachieved. Applications can therefore run in a stretched cluster modewithout massive expenditures upfront and high availability may beachieved without the need for multiple, distinctly located on-premisesstorage systems to be purchased, maintained, and so on. Readers willappreciate that user interfaces or similar mechanisms may be designedthat initiate many of the functions described herein, such that enablingapplications may be scaled into the cloud by performing a single mouseclick.

Through the use of storage systems that consist of storage that isoffered by a cloud services provider, system restores may also beoffered as a service. In such an example, point-in-time copies ofdatasets, managed objects, and other entities that may reside on anon-premises storage system may be synchronously replicated to a storagesystem whose resources are offered by a cloud services provider. In suchan example, if the need arises to restore a storage system back to aparticular point-in-time, the point-in-time copies of datasets and othermanaged objects that are contained on the storage system whose resourcesare offered by a cloud services provider may be used to restore astorage system.

Through the use of storage systems that consist of resources that areoffered by a cloud services provider, data that is stored on anon-premises storage system may be natively piped into the cloud for useby various cloud services. In such an example, the data that is in itsnative format as it was stored in the on-premises storage system, may becloned and converted into a format that is usable for various cloudservices. For example, data that is in its native format as it wasstored in the on-premises storage system may be cloned and convertedinto a format that is used by Amazon™ Redshift such that data analysisqueries may be performed against the data. Likewise, data that is in itsnative format as it was stored in the on-premises storage system may becloned and converted into a format that is used by Amazon™ DynamoDB,Amazon™ Aurora, or some other cloud database service. Because suchconversions occurs outside of the on-premises storage system, resourceswithin the on-premises storage system may be preserved and retained foruse in servicing I/O operations while cloud resources that can bespun-up as needed will be used to perform the data conversion, which maybe particularly valuable in embodiments where the on-premises storagesystem operates as the primary servicer of I/O operations and thestorage systems that consist of resources that are offered by a cloudservices provider operates as more of a backup storage system. In fact,because managed objects may be synchronized across storage systems, inembodiments where an on-premises storage system was initiallyresponsible for carrying out the steps required in an extract,transform, load (‘ETI’) pipeline, the components of such a pipeline maybe exported to a cloud and run in a cloud environment. Through the useof such techniques, analytics as a service may also be offered,including using point-in-time copies of the dataset (i.e., snapshots) asinputs to analytics services.

Readers will appreciate that applications can run on any of the storagesystems described above, and in some embodiments, such applications canrun on a primary controller, a secondary controller, or even on bothcontrollers at the same time. Examples of such applications can includeapplications doing background batched database scans, applications thatare doing statistical analysis of run-time data, and so on.

Example embodiments are described largely in the context of a fullyfunctional computer system. Readers of skill in the art will recognize,however, that the present disclosure also may be embodied in a computerprogram product disposed upon computer readable storage media for usewith any suitable data processing system. Such computer readable storagemedia may be any storage medium for machine-readable information,including magnetic media, optical media, or other suitable media.Examples of such media include magnetic disks in hard drives ordiskettes, compact disks for optical drives, magnetic tape, and othersas will occur to those of skill in the art. Persons skilled in the artwill immediately recognize that any computer system having suitableprogramming means will be capable of executing the steps of the methodas embodied in a computer program product. Persons skilled in the artwill recognize also that, although some of the example embodimentsdescribed in this specification are oriented to software installed andexecuting on computer hardware, nevertheless, alternative embodimentsimplemented as firmware or as hardware are well within the scope of thepresent disclosure.

Embodiments can include be a system, a method, and/or a computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentdisclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to some embodimentsof the disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Readers will appreciate that the steps described herein may be carriedout in a variety ways and that no particular ordering is required. Itwill be further understood from the foregoing description thatmodifications and changes may be made in various embodiments of thepresent disclosure without departing from its true spirit. Thedescriptions in this specification are for purposes of illustration onlyand are not to be construed in a limiting sense. The scope of thepresent disclosure is limited only by the language of the followingclaims.

What is claimed is:
 1. A method comprising: determining a change inavailability of a mediation service, used in a first fault responsemodel, that is external to a plurality of storage systems; and based onthe determination, communicating a different fault response model amongthe plurality of storage systems.
 2. The method of claim 1, wherein theplurality of storage systems synchronously replicate a dataset, whereinthe different fault response model specifies that a subset of theplurality of storage systems is designated to remain online and incommunication with each other for the synchronously replicated datasetto remain online, and wherein if a given storage system not within thesubset of storage systems is in communication with the subset of storagesystems after a fault, then the given storage system continues tosynchronously replicate the dataset.
 3. The method of claim 2, whereinthe subset of storage systems is selected based on pre-definedpreferences.
 4. The method of claim 2, further comprising: selecting, independence upon one or more factors, an alternate mediation service fromamong a list of mediation services; and communicating the alternatemediation service as part of communicating the different fault responsemodel.
 5. The method of claim 4, further comprising: switching,responsive to determining a reliable connection to the alternatemediation service, from the subset of storage systems for handlingmediation to the alternate mediation service for handling mediation. 6.The method of claim 1, wherein the different fault response model isselected based on one or more of: host connectivity, host location,current workload, network paths or network latency between hosts andindividual storage systems, storage system hardware characteristics. 7.The method of claim 4, wherein the subset of storage systems aredetermined based upon tracking information for applications or servicesoperating on a host computing device and a measure of impact due to lossof communication to a given storage system among the plurality ofstorage systems.
 8. A storage system comprising a computer processor anda computer memory operatively coupled to the computer processor, thecomputer memory storing computer program instructions that, whenexecuted by the computer processor, cause the storage system to carryout the steps of: determining a change in availability of a mediationservice, used in a first fault response model, that is external to aplurality of storage systems; and based on the determination,communicating a different fault response model among the plurality ofstorage systems.
 9. The storage system of claim 8, wherein the pluralityof storage systems synchronously replicate a dataset, wherein thedifferent fault response model specifies that a subset of the pluralityof storage systems is designated to remain online and in communicationwith each other for the synchronously replicated dataset to remainonline, and wherein if a given storage system not within the subset ofstorage systems is in communication with the subset of storage systemsafter a fault, then the given storage system continues to synchronouslyreplicate the dataset.
 10. The storage system of claim 9, wherein thesubset of storage systems is selected based on pre-defined preferences.11. The storage system of claim 9, wherein the computer instructions,when executed by the computer processor, further cause the storagesystem to carry out the steps of: selecting, in dependence upon one ormore factors, an alternate mediation service from among a list ofmediation services; and communicating the alternate mediation service aspart of communicating the different fault response model.
 12. Thestorage system of claim 11, wherein the computer instructions, whenexecuted by the computer processor, further cause the storage system tocarry out the steps of: switching, responsive to determining a reliableconnection to the alternate mediation service, from the subset ofstorage systems for handling mediation to the alternate mediationservice for handling mediation.
 13. The storage system of claim 11,wherein the different fault response model is selected based on one ormore of: host connectivity, host location, current workload, networkpaths or network latency between hosts and individual storage systems,storage system hardware characteristics.
 14. The storage system of claim11, wherein the subset of storage systems are determined based upontracking information for applications or services operating on a hostcomputing device and a measure of impact due to loss of communication toa given storage system among the plurality of storage systems.
 15. Anapparatus comprising a computer processor and a computer memoryoperatively coupled to the computer processor, the computer memorystoring computer program instructions that, when executed by thecomputer processor, cause the apparatus to carry out the steps of:determining a change in availability of a mediation service, used in afirst fault response model, that is external to a plurality of storagesystems; and based on the determination, communicating a different faultresponse model among the plurality of storage systems.
 16. The apparatusof claim 15, wherein the plurality of storage systems synchronouslyreplicate a dataset, wherein the different fault response modelspecifies that a subset of the plurality of storage systems isdesignated to remain online and in communication with each other for thesynchronously replicated dataset to remain online, and wherein if agiven storage system not within the subset of storage systems is incommunication with the subset of storage systems after a fault, then thegiven storage systems continues to synchronously replicate the dataset.17. The apparatus of claim 16, wherein the subset of storage systems isselected based on pre-defined preferences.
 18. The apparatus of claim16, wherein the computer program instructions, when executed by thecomputer processor, further cause the apparatus to carry out the stepsof: selecting, in dependence upon one or more factors, an alternatemediation service from among a list of mediation services; andcommunicating the alternate mediation service as part of communicatingthe different fault response model.
 19. The apparatus of claim 18,wherein the computer program instructions, when executed by the computerprocessor, further cause the apparatus to carry out the steps of:switching, responsive to determining a reliable connection to thealternate mediation service, from the subset of storage systems forhandling mediation to the alternate mediation service for handlingmediation.
 20. The apparatus of claim 15, wherein the different faultresponse model is selected based on one or more of: host connectivity,host location, current workload, network paths or network latencybetween hosts and individual storage systems, storage system hardwarecharacteristics.